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Hiring has always been a challenge, but in today’s competitive market, it feels tougher than ever. The best candidates often juggle multiple offers, and companies that move too slowly lose out. On top of that, recruiters spend hours on repetitive work — scanning resumes, coordinating interviews, chasing paperwork.
Author
Medha Bisht
November 18, 2025
3 min read
Why recruiting automation matters today
Hiring has always been a challenge, but in today’s competitive market, it feels tougher than ever. The best candidates often juggle multiple offers, and companies that move too slowly lose out. On top of that, recruiters spend hours on repetitive work — scanning resumes, coordinating interviews, chasing paperwork.
This is where recruiting automation steps in. What was once considered a niche HR tool has now become a business essential. Done right, automation doesn’t replace human recruiters. Instead, it makes them more effective by freeing them from manual tasks so they can focus on building relationships and making smarter hiring decisions.
What recruiting automation really means
At its core, recruiting automation uses technology to handle tasks that recruiters traditionally did by hand. Think of activities like sourcing candidates, screening resumes, scheduling interviews, sending reminders, or even creating onboarding documents.
This idea is part of a bigger trend called hyperautomation, where multiple technologies like AI, machine learning, and robotic process automation come together to streamline entire workflows. In recruiting, it means integrating tools so that everything from finding talent to managing employee records connects smoothly. The real power lies in building an end-to-end system where data flows seamlessly across HR and business platforms. This way, hiring isn’t just a standalone process but part of the organization’s larger growth strategy.
How AI recruiting automation delivers results
The business case for AI recruiting automation isn’t just about saving effort — it’s about measurable returns.
Cutting time-to-hire
Speed is critical. The average time-to-hire in 2025 is 36 days, which leaves plenty of room for improvement. Companies like United HR Solutions showed how AI platforms reduced time-to-hire by 45% and time-to-fill by 47%. In many cases, automation slashes hiring time by 30–50%.
When candidates receive faster responses and quick offers, companies avoid losing them to competitors. This also reduces the cost of vacant positions and boosts candidate satisfaction.
Reducing cost-per-hire
Hiring is expensive. Globally, the average cost per hire is around $4,683 when factoring in ads, recruiter hours, and agency fees. Manual scheduling alone can eat up five hours per candidate.
Automation cuts these costs significantly. Studies show administrative overhead can drop by up to 80%. Some reports estimate that AI recruiters can save as much as $16,000 per hire, thanks to faster shortlisting and reduced manual screening.
Another advantage: while manual costs rise with the number of hires, automated systems stay stable, making them ideal for fast-growing companies.
Improving candidate quality
Automation also raises the bar on candidate quality. AI tools focus on skills and experience, reducing unconscious bias and creating a fairer process. Resume-screening accuracy can reach 85–95%, far higher than manual reviews.
Case studies show a 40% boost in candidate quality scores and a 36% rise in sourcing quality after automation. Hiring better-fit employees lowers turnover, saving money and building stronger teams.
Enhancing candidate experience
Today’s candidates expect fast, transparent communication. Automation ensures they get it. Chatbots answer questions 24/7, automated emails provide updates, and scheduling tools let candidates book interviews at their convenience.
Companies using these tools report a 49% drop in candidate drop-off and a 44% increase in satisfaction. For example, the American Heart Association doubled its sourcing activity and boosted recruiter engagement by 50% after cutting administrative work with automation.
Smarter tools: the HackerEarth example
Automation isn’t one-size-fits-all. Some platforms are designed for specific industries. HackerEarth, for instance, specializes in tech hiring.
Best practices for recruiting automation
Adopting recruiting automation requires more than just buying software. Success depends on strategy and people.
Choosing the right platform
Pick tools that are scalable, easy to use, and able to integrate with your HR stack.
Building seamless integrations
An Applicant Tracking System (ATS) often serves as the hub. The best setups integrate with CRMs, payroll, and learning platforms. Tools like Zapier help connect different apps into a unified workflow.
Managing change and training teams
Resistance is common. Recruiters may worry about losing relevance or struggling with new tools. The solution is open communication and involvement. Bringing teams into the process early can increase adoption success rates. Hands-on training and continuous learning opportunities ease fears and ensure recruiters can fully use the new system.
The future of recruiting automation
The new Role of recruiters
Contrary to fears, AI will not replace recruiters. Instead, it will reshape their role. The best outcomes will come from a human-AI hybrid model. Recruiters will be able to focus more on relationship-building, candidate engagement, and employer branding, while automation provides efficiency and insights. Those who embrace this partnership will be the most successful in the talent market of the future.
Conclusion: The smarter way forward
Recruiting automation is no longer optional. It speeds up hiring, cuts costs, improves candidate quality, and enhances the overall experience. It’s about creating a partnership where automation handles the repetitive work, and recruiters focus on what they do best: building connections and making smart, strategic choices.
As competition for talent grows, the companies that thrive will be the ones that adopt automation thoughtfully and use it to empower their people. The message is clear: the future of hiring is human and automated — working together to create stronger, smarter organizations.
FAQs on recruiting automation
How does automation improve candidate experience?
By giving faster responses, consistent updates, and convenient scheduling. Chatbots answer questions anytime, and candidates can book interviews without delays. This respect for their time builds trust and strengthens employer branding.
Can automation replace human recruiters?
No. Automation is great for repetitive, high-volume tasks like screening or scheduling. But recruiters bring empathy, judgment, and cultural insight that machines can’t replicate. The future is about working together, not replacement.
I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.
My Learning Journey
I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."
Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.
The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.
But was it actually good code?
I had no idea. That's when I decided to look at the evaluation metrics
What I Thought Was "Good Code"
A working image carousel with:
Clean, semantic HTML
Smooth CSS transitions
Keyboard navigation support
ARIA labels for accessibility
Error handling for failed images
It looked like something a senior developer would write. But I had questions:
Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?
Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code
What VibeCodeArena's Evaluation Showed
The platform's objective evaluation revealed issues I never would have spotted:
Security Vulnerabilities (The Scary Ones)
No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.
Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.
Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.
SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.
Performance Problems (The Silent Killers)
DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.
Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.
Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:
Each of these seems minor, but together they compound into a poor user experience.
Code Quality Issues (The Technical Debt)
High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.
Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.
Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.
Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.
Missing Best Practices (The Professional Touches)
The platform also flagged missing elements that separate hobby projects from professional code:
No 'use strict' directive in JavaScript
Missing package.json for dependency management
No test files
Missing README documentation
No .gitignore or version control setup
Could use functional array methods for cleaner code
Missing CSS animations for enhanced UX
The "Aha" Moment
Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"
The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.
VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.
My New Workflow: The Learning Loop
This is when I discovered the real power of the platform. Here's my process now:
Step 1: Generate Code Using VibeCodeArena
I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.
Step 2: Analyze Across Several Metrics
I can get comprehensive analysis across:
Security vulnerabilities
Performance/Efficiency issues
Performance optimization opportunities
Code Quality improvements
This is where I learn. Each issue includes explanation of why it matters and how to fix it.
Step 3: Click "Challenge" and Improve
Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.
Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?
I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.
For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.
Step 4: Submit for Evaluation
After making improvements, I submit my code for evaluation. Now I see:
What actually improved (and by how much)
What new issues I might have introduced
Where I still have room to grow
Step 5: Hey, I Can Beat AI
My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.
Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.
So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.
Every iteration makes me better at both evaluating AI code and writing better prompts.
What This Means for Learning to Code with AI
This experience taught me three critical lessons:
1. Working ≠ Good Code
AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.
The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.
2. Improvement Requires Measurement
I used to iterate on code blindly: "This seems better... I think?"
Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.
Measurement transforms vague improvement into concrete progress.
3. Competition Accelerates Learning
The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.
This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.
How the Platform Helps Me Become A Better Programmer
VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:
Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.
Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.
Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.
Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.
Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.
What I've Learned So Far
Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:
How to implement Content Security Policy correctly
Why DOM depth matters for rendering performance
What CSS containment does and when to use it
How to structure code for better maintainability
Which performance optimizations actually make a difference
Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.
The Bottom Line
AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.
✓ Objective analysis that shows you what's actually wrong ✓ Educational feedback that explains why it matters ✓ A "Challenge" system that turns learning into action ✓ Measurable improvement tracking so you know what works ✓ Competitive motivation through leaderboards
My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.
The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.
Here is a link to the code for the image carousal I used for my learning journey
Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.
From Machine Language to Natural Language
I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c
The Promise and the Pitfalls
I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.
AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.
The Economic Impact
The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation. Seizing the Opportunity
Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.
This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.
Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.
In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.
The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.
Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.
In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.
As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.
Cheating in Online Assessments is a High Stakes Problem
With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.
The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.
As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.
The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "
This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.
Common Cheating Tactics and How You Can Combat Them
Using ChatGPT and other AI tools to write code
Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. At the same time, some organizations complement their process with context-aware code security support to ensure AI-generated solutions follow secure development practices.
Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
Debugging code
Optimizing existing code
Writing problem-specific code from scratch
Ways to prevent it
Enable full-screen mode
Disable copy-and-paste functionality
Restrict tab switching outside of code editors
Use AI to detect code that has been copied and pasted
Enlist external help to complete the assessment
Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.
In extreme cases, some candidates might have an off-camera individual present in the same environment for help.
Ways to prevent it
Verify a candidate using video authentication
Restrict test access from specific IP addresses
Use online proctoring by taking snapshots of the candidate periodically
Use a 360 degree environment scan to ensure no unauthorized individual is present
Using multiple devices at the same time
Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.
By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.
Ways to prevent it
Track mouse exit count to detect irregularities
Detect when a new device or peripheral is connected
Use network monitoring and scanning to detect any smart devices in proximity
Conduct a virtual whiteboard interview to monitor movements and gestures
Using remote desktop software and virtual machines
Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.
Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.
With remote desktops, candidates can screen share the test window and use external help.
Ways to prevent it
Restrict access to virtual machines
AI-based proctoring for identifying malicious keystrokes
Use smart browsers to block candidates from using VMs
Future-proof Your Online Assessments With HackerEarth
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With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
Secure, sealed-off testing environment
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Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
Former employees who return to work with the same organisation are essential assets. In talent acquisition, such employees are also termed as ‘Boomerang employees’. Former employees are valuable because they require the least training and onboarding because of their familiarity with the organization’s policies. Rehiring former employees by offering them more perks is a mark of a successful hiring process. This article will elaborate on the talent acquisition strategies for rehiring former employees, supported by a few real-life examples and best practices.
Why Should Organizations Consider Rehiring?
One of the best ways of ensuring quality hire with a low candidate turnover is to deploy employee retention programs like rehiring female professionals who wish to return to work after a career break. This gives former employees a chance to prove their expertise while ensuring them the organization’s faith in their skills and abilities. Besides, seeing former employees return to their old organizations encourages newly appointed employees to be more productive and contribute to the overall success of the organization they are working for. A few other benefits of rehiring old employees are listed below.
Reduced Hiring Costs
Hiring new talent incurs a few additional costs. For example, tasks such as sourcing resumes of potential candidates, reaching out to them, conducting interviews and screenings costs money to the HR department. Hiring former employees cuts down these costs and aids a seamless transition process for them.
Faster Onboarding
Since boomerang employees are well acquainted with the company’s onboarding process, they don’t have to undergo the entire exercise. A quick, one-day session informing them of any recent changes in the company’s work policies is sufficient to onboard them.
Retention of Knowledge
As a former employee, rehired executives have knowledge of the previous workflows and insights from working on former projects. This can be valuable in optimizing a current project. They bring immense knowledge and experience with them which can be instrumental in driving new projects to success.Starbucks is a prime example of a company that has successfully leveraged boomerang employees. Howard Schultz, the company's CEO, left in 2000 but returned in 2008 during a critical time for the firm. His leadership was instrumental in revitalizing the brand amid financial challenges.
Best Practices for Rehiring Former Employees
Implementing best practices is the safest way to go about any operation. Hiring former employees can be a daunting task especially if it involves someone who was fired previously. It is important to draft certain policies around rehiring former employees. Here are a few of them that can help you to get started.
1. Create a Clear Rehire Policy
While considering rehiring a former employee, it is essential to go through data indicating the reason why they had to leave in the first place. Any offer being offered must supersede their previous offer while marking clear boundaries to maintain work ethics. Offer a fair compensation that justifies their skills and abilities which can be major contributors to the success of the organization. A well-defined policy not only streamlines the rehiring process but also promotes fairness within the organization.
2. Conduct Thorough Exit Interviews
Exit interviews provide valuable insights into why employees leave and can help maintain relationships for potential future rehires. Key aspects to cover include:
Reasons for departure.
Conditions under which they might consider returning.
Feedback on organizational practices.
Keeping lines of communication open during these discussions can foster goodwill and encourage former employees to consider returning when the time is right.
3. Maintain Connections with Alumni
Creating and maintaining an alumni association must be an integral part of HR strategies. This exercise ensures that the HR department can find former employees in times of dire need and indicates to former employees how the organization is vested in their lives even after they have left them. This gesture fosters a feeling of goodwill and gratitude among former hires. Alumni networks and social media groups help former employees stay in touch with each other, thus improving their interpersonal communication.Research indicates that about 15% of rehired employees return because they maintained connections with their former employers.
4. Assess Current Needs Before Reaching Out
Before reaching out to former employees, assess all viable options and list out the reasons why rehiring is inevitable. Consider:
Changes in job responsibilities since their departure.
Skills or experiences gained by other team members during their absence.
It is essential to understand how the presence of a boomerang employee can be instrumental in solving professional crises before contacting them. It is also important to consider their present circumstances.
5. Initiate an Honest Conversation
When you get in touch with a former employee, it is important to understand their perspective on the job being offered. Make them feel heard and empathize with any difficult situations they may have had to face during their time in the organization. Understand why they would consider rejoining the company. These steps indicate that you truly care about them and fosters a certain level of trust between them and the organization which can motivate them to rejoin with a positive attitude.
6. Implement a Reboarding Program
When a former employee rejoins, HR departments must ensure a robust reboarding exercise is conducted to update them about any changes within the organization regarding the work policies and culture changes, training them about any new tools or systems that were deployed during their absence and allowing them time to reconnect with old team members or acquaint with new ones.
7. Make Them Feel Welcome
Creating a welcoming environment is essential for helping returning employees adjust smoothly. Consider:
Organizing team lunches or social events during their first week.
Assigning a mentor or buddy from their previous team to help them reacclimate.
Providing resources that facilitate learning about any organizational changes.
A positive onboarding experience reinforces their decision to return and fosters loyalty.
Real-Life Examples of Successful Rehiring
Several companies have successfully implemented these strategies:
IBM: The tech giant has embraced boomerang hiring by actively reaching out to former employees who possess critical skills in emerging technologies. IBM has found that these individuals often bring fresh perspectives that contribute significantly to innovation7.
Zappos: Known for its strong company culture, Zappos maintains an alumni network that keeps former employees engaged with the brand. This connection has led to numerous successful rehiring instances, enhancing both morale and productivity within teams6.
Conclusion
Rehiring former employees can provide organizations with unique advantages, including reduced costs, quicker onboarding, and retained knowledge. By implementing strategic practices—such as creating clear policies, maintaining connections, assessing current needs, and fostering welcoming environments—companies can effectively tap into this valuable talent pool.
As organizations continue navigating an ever-changing workforce landscape, embracing boomerang employees may be key to building resilient teams equipped for future challenges. By recognizing the potential benefits and following best practices outlined above, businesses can create a robust strategy for rehiring that enhances both employee satisfaction and organizational performance.
Tech Hiring Insights
HackerEarth Blogs
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Recruit, HackerEarth’s technical recruitment software, allows companies to use online coding tests to automate their tech screening process.
With a library of more than 15,000 questions, technical leads, and even non-tech recruiters can conduct tests on a large scale to grade developers for virtually any technical role.
Supporting 35+, Recruit auto-assesses the submissions of each developer instantly based on defined parameters such as logical correctness, time-efficiency, memory-efficiency, and code quality.
Tech recruiters can then analyze each applicant’s performance with the detailed reporting and analytics features within Recruit.
With its proctoring measures and plagiarism detection techniques, recruiters can be surer about the originality of each submission. -
An award-winning social recruiting platform, Open Web aggregates profiles from over 180+ social sites to give you tech talent with hard-to-find skills.Tech recruiters can build a tech pro’s profile from digital signatures gathered from these social sites. Dice Open Web also helps them to reach out to passive candidates and get better response rates, saving time and cost.
This recruiting platform offers predictive analytics to increase the efficiency of the hiring process.
Talent acquisition and hiring managers can also get an overview of the candidates' technical aptitude as Open Web focuses on portals such as GitHub and Stack Overflow.
Codility offers an intuitive recruiting platform to increase brand visibility and help source programmers to add value to your company. Developers can be evaluated, or inspired, using customized tests/challenges and interviewed via the automated platform.
Utah-based HireVue calls its product an “all-in-one video interview and pre-hire assessment solution.”The digital interview platform helped recruiters choose applicants from a sea of resumes by watching videos where they had recorded responses to interview questions.The company now adds artificial intelligence (voice recognition software, licensed facial recognition software, a ranking algorithm) to pick the ideal candidate.
HireVue promises tech recruiters a modern, simple approach to hiring through insightful data.
Using neuroscience games and AI, Pymetrics offers a bold recruiting platform that is bias-free.It helps tech recruiters build a profile of a candidate not based on resumes but on their emotional and cognitive traits.Pymetrics identifies what candidates are best at and matches them to the right jobs; this approach puts applicants on a more equal footing.“If LinkedIn and Match.com could have a child, Pymetrics would be it.” (Digital Trends)
This search engine from Workable helps tech recruiters source candidates using “information aggregated from multiple sources in real-time,” streamline applicant tracking processes and manage interviews.People Search helps personalize reach and boost response rates. It allows Boolean queries as well.
This is a behavioral assessment designed to be an effective, simple, and easy evaluation of existing and future employee work skills.The proven methodology helps tech recruiters define the cognitive and behavior requirements for a job and assess and hire candidates accurately.The test uses a free-choice format and is not timed; it takes about six minutes and measures four constructs: extroversion, dominance, patience, and formality.
Devskiller lets companies use their own code base to test programmers online and lets developers use their own IDEs and resources.Tech recruiters can screen applicants with real-world sample tests to assess what really matters and interview them in real time.The recruiter-friendly solution automatically measures the coding skills and finds the real problem solvers. The company says it aims to imitate a “first day at work experience.”
Hired brings together tech recruiters and employees, matching the right people to the right jobs.The website offers “algorithmic matching, key ATS integrations, and 1:1 support” to make smart recruiting decisions for employers looking for top quality technical talent.
This artificial intelligence-powered competency-based hiring platform helps recruiters build great tech teams. Glider’s approach combines the preferences and capabilities of employers (and job seekers) to ensure an efficient recruitment process without bias. For data-driven hiring decisions, Glider offers auto-scored coding tasks, video interviews, and real-world simulations.
These are only a few of the most effective and popular recruiting platform available in the market.With amazing advances in artificial intelligence and machine learning, automation almost guarantees the efficiency and accuracy of the hiring process and helps create a rich workplace.Although automation in technical recruitment is a no-brainer, organizations must remember to give enough importance to emotional intelligence and human interaction.The recruitment landscape has changed tremendously in recent years, especially with diversity and inclusion goals and the need to become “innovative” gaining prominence.Forward-thinking HR leaders must focus on optimizing talent along with strategic hiring and retaining engaged employees to boost overall business performance.It pays to take all the help you can get—use talent assessment software best suited to your needs and “transform” your recruitment strategy.
Detailed feature comparison of 8 recruiting software platform for developer hiring
We decided to compare the 8 most common recruitment software platforms as per the number of users. These comparisons have been made from an external source.
All platforms have been compared based on price, number of users (admins), number of assessments and 9 other criteria.
Download full comparison by filling the form below -hbspt.forms.create({portalId: "2586902",formId: "28743abe-765e-4f2a-b7d6-470b90136efc"});
Benchmark existing employees to identify skill gaps before hiring externally.
Adopt data-driven strategies to expedite and enhance the quality of the hiring process.
Use AI-powered tools to assess and identify top talent.
Engage with talent via hackathons and coding challenges.
Aim to provide a positive candidate experience with your recruitment methods.
Introduction
India’s technology sector has seen impeccable growth in recent years, creating exciting job opportunities for engineering professionals. However, the employment numbers reflect a different picture. Every year, roughly 1.5 million students graduate from engineering colleges. However, only 10% can find employment as they lack the practical skills to qualify for coveted jobs. Therefore, identifying, assessing and hiring top talent is challenging for even the most skilled and experienced recruiters. To tackle this issue, hiring teams must adapt data-driven strategies, leverage AI-powered tools and focus on skills-based hiring.
In this article we explore modern technology recruiting techniques that can help recruiters make faster, fairer, and more efficient hiring decisions.
The Challenges in Tech Hiring Today
Decline In Skilled Talent
Although there is no shortage of highly qualified developers in today’s job market, there is an acute shortage of ones that are well-rounded. One of the major issues recruiters face today is finding the skilled and talented developers with industry-specific skills.
A LinkedIn report found that 67% of recruiters struggle to source qualified candidates for technical positions.
Passive Hiring Is Passe
Traditional hiring methods are ineffective and are being replaced with AI and automation. Even advanced Applicant Tracking Systems (ATS) fail to capture real skills, leading to a gap between the job requirements and a candidate’s true capabilities leading to very few favourable results.
Pro tip! Use live assessment tests to assess candidate skills in real time.
Bias in Hiring Still Exists
Several companies still rely on conventional hiring methods, leading to unconscious bias, causing them to miss out on acquiring great talent. Over 60% of hiring managers admit that hiring decisions are biased at some point in the recruitment process.
Candidate Experience Matters More Than Ever
A positive candidate experience is crucial in retaining good talent. Shorter hiring periods, and prompt response after interviews are some of the ways to keep new talent engaged in the recruitment process. Lengthy assessment periods, slow feedback loops and outdated interview formats turn candidates away even from the most admired brands/companies.
Pro tip! Aim to complete the entire hiring process for top talent within 2 weeks
Overcoming Hiring Challenges with Modern Recruiting Techniques
Attracting Top Talent
The first step towards employing the best tech talent is to craft meaningful job descriptions. In tech recruitment, top talent is attracted to a purpose-driven job description over everything else. Engineers are realists. So it is essential to showcase your organisation’s tech values to attract skilled talent.
Leverage AI-Powered Screening
AI-powered recruitment tools can help recruiters find top talent without bias, automate mundane tasks, reduce hiring time and ensure diversity.
How to Implement AI-Powered Assessments:
AI-driven assessments rank candidates based on skills and problem-solving efficiency.
Automated coding interview platforms like HackerEarth FaceCodeprovide real-time code playback and instant feedback.
Reduce unconscious bias by using AI-powered resume masking to focus on skills rather than demographics.
Pro Tip: Remember that no matter how efficient AI is, at the end of the day it is a program that can produce potentially biased results. Hence, ensure that your hiring strategies include human intervention at crucial stages.
Use Hackathons to Identify Top Talent
Hackathons are an excellent way to engage with top developers while assessing their technical and collaboration skills.
How to Use Hackathon as a Hiring Tool?
Host a hiring hackathon to attract top talent and test problem-solving capabilities.
Use hackathons as a pre-hiring assessment to see how candidates perform under real-world pressure.
Engage with HackerEarth’s rich global developer community of 9.6M+ developers..
Adopt Skills-Based Hiring
Resumes fail to capture the true skills of potential candidates. Instead, use online assessment tools to understand and assess potential candidates in real time.
How to Implement Skills-Based Assessments:
Use HackerEarth Assessments to evaluate coding and problem-solving abilities.
Design real-world coding projects that mimic actual work scenarios.
Incorporate full-stack developer assessments to gauge a candidate’s overall expertise.
Enhance Candidate Experience with Seamless Processes
Leverage developer-friendly forums like GitHub and LinkedIn to attract skilful candidates. Make yourself approachable to potential candidates by leveraging chatbots to answer common questions they might have about your company. Such practices enhance employee engagement, garner good brand recognition and facilitate seamless hiring.
How to Enhance Candidate Experience?
Cut down assessment time with AI-powered adaptive testing that adjusts difficulty based on responses.
Offer live coding interviews instead of multiple rounds of generic technical screenings.
Provide instant feedback to candidates post-assessment to improve engagement.
Benchmark Internal Talent for Strategic Hiring
Implement internal feedback loops that help bridge skill gaps, and then advertise for candidates accordingly.
How to Implement:
Use internal benchmarking to measure current employee skills against industry standards.
Create personalized learning paths for upskilling developers before sourcing externally.
Encourage internal upward mobility by promoting skilled employees into open roles instead of hiring externally. It’s not only cheaper but it also increases retention and improves employee morale.
Case Study: How MoEngage Enhanced Hiring Quality with HackerEarth
Challenge: MoEngage, a customer engagement platform, wanted to add top notch talent to its engineering team quickly but found conventional screening methods misaligned and difficult to manage.
Solution: By partnering with HackerEarth, MoEngage introduced technical assessments to pre-screen candidates before technical interviews. This streamlined the hiring process and ensured that only qualified candidates progressed to the interview stages.
Key Achievements:
50% improvement in candidate quality: The introduction of assessments led to a higher calibre of candidates reaching the interview stage.
400% expansion in the talent pool: Automated assessments allowed MoEngage to consider a broader range of applicants without increasing the recruitment team's workload.
Reduction in interviews per hire: Previously, hiring managers interviewed up to 15 candidates per role; with HackerEarth's assessments, this number decreased to 6, optimising interviewer time and resources.
Results: MoEngage successfully scaled its engineering teams, improved the efficiency of its hiring process, and ensured a higher calibre of new hires, contributing to the company's growth and innovation.
The recruitment landscape is evolving rapidly. With AI, data-driven decision-making, and skills-first hiring, companies can identify and retain the best developers faster and more efficiently.
Conclusion
The tech industry is making transformative strides with the help of AI and automation. To keep up with changing times, recruiters must accept and adapt data-driven methods to identify, assess and hire skilled professionals. HackerEarth’s assessment solutions are agile and capable of helping modern-day recruiters carry out their mission of aligning top tier talent with organization’s needs. Skills-based assessments, AI-driven hiring practices, and hackathons are here to stay, and recruiters must leverage these tools to find the best tech talent in the industry.
Bonjour! Welcome to another
part of the series on data visualization techniques. In the previous two articles, we discussed different data
visualization techniques that can be applied to visualize and gather insights from categorical and continuous
variables. You can check out the first two articles here:
In this article, we’ll go through the implementation and use of a bunch of data
visualization techniques such as heat maps, surface plots, correlation plots, etc. We will also look at different
techniques that can be used to visualize unstructured data such as images, text, etc.
### Importing the required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.plotly as py
import plotly.graph_objs as go
%matplotlib inline
Heatmaps
A heat map(or
heatmap) is a two-dimensional graphical representation of the data
which uses colour to represent data points on the graph. It is useful in understanding underlying relationships
between data values that would be much harder to understand if presented numerically in a table/ matrix.
### We can create a heatmap by simply using the seaborn library.
sample_data = np.random.rand(8, 12)
ax = sns.heatmap(sample_data)
Fig 1. Heatmap using the seaborn library
Let’s understand this using an example. We’ll be using the metadata from Deep
Learning 3 challenge. Link to the dataset. Deep Learning 3 challenged the participants to predict the attributes of animals by
looking at their images.
### Training metadata contains the name of the image and the corresponding attributes associated with the animal in the image.
train = pd.read_csv('meta-data/train.csv')
train.head()
We will be analyzing how often an attribute occurs in relationship with the other
attributes. To analyze this relationship, we will compute the co-occurrence matrix.
### Extracting the attributes
cols = list(train.columns)
cols.remove('Image_name')
attributes = np.array(train[cols])
print('There are {} attributes associated with {} images.'.format(attributes.shape[1],attributes.shape[0]))
Out: There are 85 attributes associated with 12,600 images.
# Normalizing the co-occurrence matrix, by converting the values into a matrix
# Compute the co-occurrence matrix in percentage
#Reference:https://stackoverflow.com/questions/20574257/constructing-a-co-occurrence-matrix-in-python-pandas/20574460
cooccurrence_matrix_diagonal = np.diagonal(cooccurrence_matrix)
with np.errstate(divide = 'ignore', invalid='ignore'):
cooccurrence_matrix_percentage = np.nan_to_num(np.true_divide(cooccurrence_matrix, cooccurrence_matrix_diagonal))
print('\n Co-occurrence matrix percentage: \n', cooccurrence_matrix_percentage)
We can see that the values in the co-occurrence matrix represent the occurrence of
each attribute with the other attributes. Although the matrix contains all the information, it is visually hard to
interpret and infer from the matrix. To counter this problem, we will use heat maps, which can help relate the
co-occurrences graphically.
fig = plt.figure(figsize=(10, 10))
sns.set(style='white')
# Draw the heatmap with the mask and correct aspect ratio
ax = sns.heatmap(cooccurrence_matrix_percentage, cmap='viridis', center=0, square=True, linewidths=0.15, cbar_kws={"shrink": 0.5, "label": "Co-occurrence frequency"}, )
ax.set_title('Heatmap of the attributes')
ax.set_xlabel('Attributes')
ax.set_ylabel('Attributes')
plt.show()
Fig 2. Heatmap of the co-occurrence matrix indicating the frequency of occurrence of one attribute with
other
Since the frequency of the co-occurrence is represented by a colour pallet, we can
now easily interpret which attributes appear together the most. Thus, we can infer that these attributes are
common to most of the animals.
Choropleth
Choropleths are a type of map that provides an easy way to show how some quantity
varies across a geographical area or show the level of variability within a region. A heat map is similar but
doesn’t include geographical boundaries. Choropleth maps are also appropriate for indicating differences in the
distribution of the data over an area, like ownership or use of land or type of forest cover, density information,
etc. We will be using the geopandas library to implement the choropleth graph.
We will be using choropleth graph to visualize the GDP across the globe. Link
to the dataset.
# Importing the required libraries
import geopandas as gpd
from shapely.geometry import Point
from matplotlib import cm
# GDP mapped to the corresponding country and their acronyms
df =pd.read_csv('GDP.csv')
df.head()
COUNTRY
GDP
(BILLIONS)
CODE
0
Afghanistan
21.71
AFG
1
Albania
13.40
ALB
2
Algeria
227.80
DZA
3
American Samoa
0.75
ASM
4
Andorra
4.80
AND
### Importing the geometry locations of each country on the world map
geo = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))[['iso_a3', 'geometry']]
geo.columns = ['CODE', 'Geometry']
geo.head()
# Mapping the country codes to the geometry locations
df = pd.merge(df, geo, left_on='CODE', right_on='CODE', how='inner')
#converting the dataframe to geo-dataframe
geometry = df['Geometry']
df.drop(['Geometry'], axis=1, inplace=True)
crs = {'init':'epsg:4326'}
geo_gdp = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)
## Plotting the choropleth
cpleth = geo_gdp.plot(column='GDP (BILLIONS)', cmap=cm.Spectral_r, legend=True, figsize=(8,8))
cpleth.set_title('Choropleth Graph - GDP of different countries')
Fig 3. Choropleth graph indicating the GDP according to geographical locations
Surface plot
Surface plots are used for the three-dimensional representation of the data. Rather
than showing individual data points, surface plots show a functional relationship between a dependent variable (Z)
and two independent variables (X and Y).
It is useful in analyzing relationships between the dependent and the independent
variables and thus helps in establishing desirable responses and operating conditions.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter
# Creating a figure
# projection = '3d' enables the third dimension during plot
fig = plt.figure(figsize=(10,8))
ax = fig.gca(projection='3d')
# Initialize data
X = np.arange(-5,5,0.25)
Y = np.arange(-5,5,0.25)
# Creating a meshgrid
X, Y = np.meshgrid(X, Y)
R = np.sqrt(np.abs(X**2 - Y**2))
Z = np.exp(R)
# plot the surface
surf = ax.plot_surface(X, Y, Z, cmap=cm.GnBu, antialiased=False)
# Customize the z axis.
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
ax.set_title('Surface Plot')
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
One of the main applications of surface plots in machine learning or data science
is the analysis of the loss function. From a surface plot, we can analyze how the hyperparameters affect the loss
function and thus help prevent overfitting of the model.
Fig 4. Surface plot visualizing the dependent variable w.r.t the independent variables in 3-dimensions
Visualizing high-dimensional datasets
Dimensionality refers to the number of attributes present in the dataset. For
example, consumer-retail datasets can have a vast amount of variables (e.g. sales, promos, products, open, etc.).
As a result, visually exploring the dataset to find potential correlations between variables becomes extremely
challenging.
Therefore, we use a technique called dimensionality reduction to visualize higher
dimensional datasets. Here, we will focus on two such techniques :
Before we jump into understanding PCA, let’s review some terms:
Variance: Variance is simply the measure of the spread
or extent of the data. Mathematically, it is the average squared deviation from the mean position.
Covariance: Covariance is the measure of the extent to
which corresponding elements from two sets of ordered data move in the same direction. It is the measure of how
two random variables vary together. It is similar to variance, but where variance tells you the extent of one
variable, covariance tells you the extent to which the two variables vary together. Mathematically, it is
defined as:
A positive covariance means X and Y are positively related, i.e., if X increases, Y
increases, while negative covariance means the opposite relation. However, zero variance means X and Y are not
related.
Fig 5. Different types of covariance
PCA is the orthogonal projection of data onto a lower-dimension linear space that
maximizes variance (green line) of the projected data and minimizes the mean squared distance between the data
point and the projects (blue line). The variance describes the direction of maximum information while the mean
squared distance describes the information lost during projection of the data onto the lower dimension.
Thus, given a set of data points in a d-dimensional space, PCA projects these
points onto a lower dimensional space while preserving as much information as possible.
Fig 6. Illustration of principal component analysis
In the figure, the component along the direction of maximum variance is defined as
the first principal axis. Similarly, the component along the direction of second maximum variance is defined as
the second principal component, and so on. These principal components are referred to the new dimensions carrying
the maximum information.
# We will use the breast cancer dataset as an example
# The dataset is a binary classification dataset
# Importing the dataset
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X = pd.DataFrame(data=data.data, columns=data.feature_names) # Features
y = data.target # Target variable
# Importing PCA function
from sklearn.decomposition import PCA
pca = PCA(n_components=2) # n_components = number of principal components to generate
# Generating pca components from the data
pca_result = pca.fit_transform(X)
print("Explained variance ratio : \n",pca.explained_variance_ratio_)
Out: Explained variance ratio :
[0.98204467 0.01617649]
We can see that 98% (approx) variance of the data is along the first principal
component, while the second component only expresses 1.6% (approx) of the data.
# Creating a figure
fig = plt.figure(1, figsize=(10, 10))
# Enabling 3-dimensional projection
ax = fig.gca(projection='3d')
for i, name in enumerate(data.target_names):
ax.text3D(np.std(pca_result[:, 0][y==i])-i*500 ,np.std(pca_result[:, 1][y==i]),0,s=name, horizontalalignment='center', bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
# Plotting the PCA components
ax.scatter(pca_result[:,0], pca_result[:, 1], c=y, cmap = plt.cm.Spectral,s=20, label=data.target_names)
plt.show()
Fig 7. Visualizing the distribution of cancer across the data
Thus, with the help of PCA, we can get a visual perception of how the labels are
distributed across given data (see Figure).
T-distributed Stochastic Neighbour Embeddings (t-SNE) is a non-linear
dimensionality reduction technique that is well suited for visualization of high-dimensional data. It was
developed by Laurens van der Maten and Geoffrey Hinton. In contrast to PCA, which is a mathematical technique,
t-SNE adopts a probabilistic approach.
PCA can be used for capturing the global structure of the high-dimensional data but
fails to describe the local structure within the data. Whereas, “t-SNE” is capable of capturing the local
structure of the high-dimensional data very well while also revealing global structure such as the presence of
clusters at several scales. t-SNE converts the similarity between data points to joint probabilities and tries to
maximize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embeddings and
high-dimension data. In doing so, it preserves the original structure of the data.
# We will be using the scikit learn library to implement t-SNE
# Importing the t-SNE library
from sklearn.manifold import TSNE
# We will be using the iris dataset for this example
from sklearn.datasets import load_iris
# Loading the iris dataset
data = load_iris()
# Extracting the features
X = data.data
# Extracting the labels
y = data.target
# There are four features in the iris dataset with three different labels.
print('Features in iris data:\n', data.feature_names)
print('Labels in iris data:\n', data.target_names)
Out: Features in iris data:
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
Labels in iris data:
['setosa' 'versicolor' 'virginica']
# Loading the TSNE model
# n_components = number of resultant components
# n_iter = Maximum number of iterations for the optimization.
tsne_model = TSNE(n_components=3, n_iter=2500, random_state=47)
# Generating new components
new_values = tsne_model.fit_transform(X)
labels = data.target_names
# Plotting the new dimensions/ components
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
for label, name in enumerate(labels):
ax.text3D(new_values[y==label, 0].mean(),
new_values[y==label, 1].mean() + 1.5,
new_values[y==label, 2].mean(), name,
horizontalalignment='center',
bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
ax.scatter(new_values[:,0], new_values[:,1], new_values[:,2], c=y)
ax.set_title('High-Dimension data visualization using t-SNE', loc='right')
plt.show()
Fig 8. Visualizing the feature space of the iris dataset using t-SNE
Thus, by reducing the dimensions using t-SNE, we can visualize the distribution of
the labels over the feature space. We can see that in the figure the labels are clustered in their own little
group. So, if we’re to use a clustering algorithm to generate clusters using the new features/components, we can
accurately assign new points to a label.
Conclusion
Let’s quickly summarize the topics we covered. We started with the generation of
heatmaps using random numbers and extended its application to a real-world example. Next, we implemented
choropleth graphs to visualize the data points with respect to geographical locations. We moved on to implement
surface plots to get an idea of how we can visualize the data in a three-dimensional surface. Finally, we used
two- dimensional reduction techniques, PCA and t-SNE, to visualize high-dimensional datasets.
I encourage you to implement the examples described in this article to get a
hands-on experience. Hope you enjoyed the article. Do let me know if you have any feedback, suggestions, or
thoughts on this article in the comments below!
From Pope Francis to the President’s office, hackathons seem to be the flavor of the day. Over 80% of Fortune 100 and 60% of Fortune 500 companies have hosted or sponsored a hackathon. With rising popularity come criticisms and misconceptions. Having closely witnessed 300+ hackathons and multiple formats (public, corporate-sponsored, University, Internal and Non-profit hackathons) over a period of two years, one thing is absolutely clear.
Hackathon is a very powerful tool for innovation, IF DONE RIGHT.
However, there are some misbeliefs and unrealistic expectations.
Corporates exploit developers
The most common criticism is that the corporates outsource their work—the participant being unpaid labor and hackathons being exploitative in nature.
Hackathons are purely driven by passionate developers/ participants. The spirit in which people participate in hackathons is no different from voluntary contributions to open source. It originates from the desire to learn, experiment, solve complex problems, contribute, and build cool stuff.
For such developers, hackathons provide the best platform to showcase their skills, connect with their peers, seek mentorship from the industry experts and get recognized.
Case in point: The recent Tesla hackathon, which aims to solve the two major problematic bottlenecks in the robots. Tesla’s aim here is not to outsource work to unpaid labor. It is to crowdsource innovative solutions for its pressing problems.
What about the developers? Are they being exploited?
If you have the opportunity to work on cutting-edge technology for one of the world’s leading firms transforming the face of the automobile industry and take a shot at solving its most pressing issue in 48 hours, it’s more upside than downside for you. A participant has a lot to gain for the time and effort he or she invests.
Apart from the monetary rewards which only goes to a small percentage of the participants, the real benefit for these developers is often intangible. As one of the participants of the recent International Women’s Hackathon 2018 puts it:
We tried to develop an app that helps answer Google forms through voice ‘Hear me Out’. Although we were not able to build a webapp which was what we had initially thought, just a prototype of a desktop app but the process of sitting together with coming up with an idea and coding was fun. In between the fun we learnt through errors and via helping each other and taking help of seniors and peers. Coding together with chips, maggi, coffee and friends in my room from evening to night and night to morning before the submission was enlightening and enjoyable.
In over 95% of the hackathons, the IPs belong to the participants. Although a majority of the companies still do not claim IP rights for the products created at a hackathon, there are still a few companies that do.
**But we advise participants to carefully read the T&C before signing up. Companies should ensure they communicate anything that is likely to be different from the usual T&C for such events.
Here is the T&C of a recent hackathon hosted by Intel.
Participants owning the ideas/IPs created at the hackathons and companies opting to buy the best ones is, however, a practice that is mutually beneficial and welcomed.
Here is another variation of the T&C for a hackathon hosted by Procter & Gamble.
Employees are obligated to participate in internal hackathons
Companies try to squeeze out innovation out of employees by conducting hackathons and employees are often obligated to participate.
Companies often struggle to come up with ways to engage with their employees in a more meaningful way. Ask any HR Manager or People Director; it is impossible to come up with an activity that pleases every employee.
A hackathon is one particular engagement that hits the sweet spot and many use internal hackathons as a tool for driving employee engagement and fostering a culture of innovation.
Hackathon is one of the very few activities that combine the four essential components of employee engagement. An employee engagement initiative should allow the employees to tap into their passion, enable them to make meaningful contributions to the company, offer recognition, and be engaging.
There could be instances where employees participate out of peer pressure and obligation. But this is not a hackathon-specific issue. Peer pressure at the workplace is common across companies. It is important that companies ensure hackathon participation is voluntary. Constraints might help innovation but not peer pressure and feeling obliged.
Innovations rarely come out of hackathons
The innovations hardly last beyond the hackathon. GroupMe and Skype are rare occurrences and exemptions.
The aim of the hackathon is not to create a blockbuster product, conjure groundbreaking innovations, or build a multi-million company in 48 hours. If that is the expectation, then it is clearly wrong.
The objective of a hackathon is to provide an avenue for experimenting ideas, exploring opportunities, and attempting to solve problems. If a company can spot interesting concepts, promising ideas, and creative solutions, it will further go through an extensive and rigorous process of evaluation, testing, and development before it can be rolled out.
A hackathon is a tool to seed the culture of innovation and meritocracy. It abides by the principle that good ideas can come from anywhere. It is just the starting process of the long and lengthy process of innovation filled with uncertainty. This infographic will give you an idea about the role of hackathons in the process of innovation.
Not an effective recruiting tool
Unlike hiring challenges, a hackathon is not a recruitment tool and should not be used as one. Yes, sometimes companies do spot extraordinary talent and end up absorbing them. But it is just a byproduct and not a regular occurrence.
Neither feasible nor inventive
Hackathon projects are neither feasible nor inventive.
This is a common problem faced by hackathon hosts. The quality of the output does not always meet the expectation. However, over time, we found out that a few common factors affect the success of the hackathons.
Defining problem/goal
Providing the right contextual knowledge
Marketing to the right audience
Guidance and mentorship
Setting the expectations right
Conclusion
There is no perfect tool for innovation. Every process, activity, and framework has its own merits and demerits. It is important to address the drawbacks. Without participants, a hackathon is futile. Hence, it is important to ensure the participants enjoy and gain value out of hackathons.
Overall, a hackathon is a very powerful tool for innovation, IF DONE RIGHT.
Deep Learning is on the
rise, extending its application in every field, ranging from computer vision to natural language processing,
healthcare, speech recognition, generating art, addition of sound to silent movies, machine translation,
advertising, self-driving cars, etc. In this blog, we will extend the power of deep learning to the domain of
music production. We will talk about how we can use deep learning to generate new musical beats.
The current technological advancements have transformed the way we produce music,
listen, and work with music. With the advent of deep learning, it has now become possible to generate music
without the need for working with instruments artists may not have had access to or the skills to use previously.
This offers artists more creative freedom and ability to explore different domains of music.
Recurrent Neural Networks
Since music is a sequence of notes and chords, it doesn’t have a fixed
dimensionality. Traditional deep neural network techniques cannot be applied to generate music as they assume the
inputs and targets/outputs to have fixed dimensionality and outputs to be independent of each other. It is
therefore clear that a domain-independent method that learns to map sequences to sequences would be useful.
Recurrent neural networks (RNNs) are a class of artificial neural
networks that make use of sequential information present in the data.
Fig. 1 A basic RNN unit.
A recurrent neural network has looped, or recurrent, connections which allow the network
to hold information across inputs. These connections can be thought of as memory cells. In other words, RNNs can
make use of information learned in the previous time step. As seen in Fig. 1, the output of the previous
hidden/activation layer is fed into the next hidden layer. Such an architecture is efficient in learning
sequence-based data.
In this blog, we will be using the Long Short-Term Memory (LSTM)
architecture. LSTM is a type of recurrent neural network (proposed by Hochreiter and Schmidhuber, 1997) that can
remember a piece of information and keep it saved for many timesteps.
Dataset
Our dataset includes piano tunes stored in the
MIDI format. MIDI (Musical Instrument Digital Interface) is a protocol which allows electronic
instruments and other digital musical tools to communicate with each other. Since a MIDI file only represents
player information, i.e., a series of messages like ‘note on’, ‘note off, it is more compact, easy to modify, and
can be adapted to any instrument.
Before we move forward, let us understand some music
related terminologies:
Note: A note is either a single
sound or its representation in notation. Each note consist of pitch, octave, and an offset.
Pitch: Pitch refers to the frequency
of the sound.
Octave: An octave is the interval
between one musical pitch and another with half or double its frequency.
Offset: Refers to the location of
the note.
Chord: Playing multiple notes at the
same time constitutes a chord.
Data Preprocessing
We will use the music21 toolkit (a toolkit for
computer-aided musicology, MIT) to extract data from these MIDI files.
Notes Extraction
def get_notes():
notes = []
for file in songs:
# converting .mid file to stream object
midi = converter.parse(file)
notes_to_parse = []
try:
# Given a single stream, partition into a part for each unique instrument
parts = instrument.partitionByInstrument(midi)
except:
pass
if parts: # if parts has instrument parts
notes_to_parse = parts.parts[0].recurse()
else:
notes_to_parse = midi.flat.notes
for element in notes_to_parse:
if isinstance(element, note.Note):
# if element is a note, extract pitch
notes.append(str(element.pitch))
elif(isinstance(element, chord.Chord)):
# if element is a chord, append the normal form of the
# chord (a list of integers) to the list of notes.
notes.append('.'.join(str(n) for n in element.normalOrder))
with open('data/notes', 'wb') as filepath:
pickle.dump(notes, filepath)
return notes
The function get_notes returns a list of notes and chords present in the .mid file.
We use the converter.parse function to convert the midi file in a stream object, which in turn is used to
extract notes and chords present in the file. The list returned by the function get_notes() looks as
follows:
We can see that the list consists of pitches and chords (represented as a list of
integers separated by a dot). We assume each new chord to be a new pitch on the list. As letters are used to
generate words in a sentence, similarly the music vocabulary used to generate music is defined by the unique
pitches in the notes list.
Generating Input and Output Sequences
A neural network accepts only real values as input and since the pitches in the notes
list are in string format, we need to map each pitch in the notes list to an integer. We can do so as follows:
# Extract the unique pitches in the list of notes.
pitchnames = sorted(set(item for item in notes))
# create a dictionary to map pitches to integers
note_to_int = dict((note, number) for number, note in enumerate(pitchnames))
Next, we will create an array of input and output sequences to train our model. Each
input sequence will consist of 100 notes, while the output array stores the 101st note for the corresponding
input sequence. So, the objective of the model will be to predict the 101st note of the input sequence of notes.
# create input sequences and the corresponding outputs
for i in range(0, len(notes) - sequence_length, 1):
sequence_in = notes[i: i + sequence_length]
sequence_out = notes[i + sequence_length]
network_input.append([note_to_int[char] for char in sequence_in])
network_output.append(note_to_int[sequence_out])
Next, we reshape and normalize the input vector sequence before feeding it to the
model. Finally, we one-hot encode our output vector.
n_patterns = len(network_input)
# reshape the input into a format compatible with LSTM layers
network_input = np.reshape(network_input, (n_patterns, sequence_length, 1))
# normalize input
network_input = network_input / float(n_vocab)
# One hot encode the output vector
network_output = np_utils.to_categorical(network_output)
Model Architecture
We will use keras to build our model architecture. We use
a character level-based architecture to train the model. So each input note in the music file is used to predict
the next note in the file, i.e., each LSTM cell takes the previous layer activation (a⟨t−1⟩) and the previous layers actual output (y⟨t−1⟩) as input at the current time step tt. This is depicted in the following figure (Fig 2.).
Our music model consists of two LSTM layers with each
layer consisting of 128 hidden layers. We use ‘categorical cross entropy‘ as the loss function and
‘adam‘ as the optimizer. Fig. 3 shows the model summary.
Fig 3. Model summary
Model Training
To train the model, we call the
model.fit function with the input and output sequences as the input to the function. We also
create a model checkpoint which saves the best model weights.
The train_network method gets the notes, creates
the input and output sequences, creates a model, and trains the model for 200 epochs.
Music Sample Generation
Now that we have trained our model, we can use it to
generate some new notes. To generate new notes, we need a starting note. So, we randomly pick an integer and pick
a random sequence from the input sequence as a starting point.
def generate_notes(model, network_input, pitchnames, n_vocab):
""" Generate notes from the neural network based on a sequence of notes """
# Pick a random integer
start = np.random.randint(0, len(network_input)-1)
int_to_note = dict((number, note) for number, note in enumerate(pitchnames))
# pick a random sequence from the input as a starting point for the prediction
pattern = network_input[start]
prediction_output = []
print('Generating notes........')
# generate 500 notes
for note_index in range(500):
prediction_input = np.reshape(pattern, (1, len(pattern), 1))
prediction_input = prediction_input / float(n_vocab)
prediction = model.predict(prediction_input, verbose=0)
# Predicted output is the argmax(P(h|D))
index = np.argmax(prediction)
# Mapping the predicted interger back to the corresponding note
result = int_to_note[index]
# Storing the predicted output
prediction_output.append(result)
pattern.append(index)
# Next input to the model
pattern = pattern[1:len(pattern)]
print('Notes Generated...')
return prediction_output
Next, we use the trained model to predict the next 500
notes. At each time step, the output of the previous layer (ŷ⟨t−1⟩) is provided as input (x⟨t⟩) to the LSTM layer at the current
time step t. This is depicted in the following figure (see Fig. 4).
Fig 4. Sampling from a trained network.
Since the predicted output is an array of probabilities,
we choose the output at the index with the maximum probability. Finally, we map this index to the actual note and
add this to the list of predicted output. Since the predicted output is a list of strings of notes and chords, we
cannot play it. Hence, we encode the predicted output into the MIDI format using the create_midi method.
### Converts the predicted output to midi format
create_midi(prediction_output)
To create some new jazz music, you can simply call the generate() method, which
calls all the related methods and saves the predicted output as a MIDI file.
#### Generate a new jazz music
generate()
Out:
Initiating music generation process.......
Loading Model weights.....
Model Loaded
Generating notes........
Notes Generated...
Saving Output file as midi....
To play the generated MIDI in the Jupyter Notebook you
can import the play_midi method from the play.py file or use an external MIDI player or convert
the MIDI file to the mp3. Let’s listen to our generated jazz piano music.
### Play the Jazz music
play.play_midi('test_output3.mid')
“Generated Track 1”
Deep LearningRecurrent Neural Network
Congratulations! You can now generate your own jazz
music. You can find the full code in this Github repository. I encourage you to play
with the parameters of the model and train the model with input sequences of different sequence lengths. Try to
implement the code for some other instrument (such as guitar). Furthermore, such a character-based model can also
be applied to a text corpus to generate sample texts, such as a poem.
Also, you can showcase your own personal composer and any similar idea in the World
Music Hackathonby HackerEarth.
Have anything to say? Feel free to comment below for any
questions, suggestions, and discussions related to this article. Till then, happy coding.
Music is the universal language of mankind—a great uniter. It’s astonishing how music can connect souls, overcome barriers, and bring people closer. It is something that people who differ on anything and everything can have in common.
The World Music Hackathon is a festival of music, innovation, and creativity. We are pushing down the boundaries between “hacking” and “music” to bring the music and tech world together. There are no limits to what you can create; we encourage hacking of music in the broadest conceivable sense, for example, through instrument-building, data visualization, collaboration, improvisation, or any other way you can imagine.
There are craftsmen, researchers, and other music programmers who are doing great work in the field of music, however, they are not getting the consideration they merit for whatever reason.
This is your platform to change the future of the underserved music community and is by no means is limited to young and old, regional or cultural genres or gender identity. It is a platform that can induce diversity across backgrounds, perspectives, and abilities to drive personal growth through creation, collaboration, and communication.
Music is becoming more digital every day. What's more, the World Music Hackathon is the phase to explore different avenues regarding its progression and create thoughts for the future of music and music groups.
Your ideas can connect the artist with his or her audience, on- and offline, real-time or over time. Your ideas will interface the path in rethinking and re-engineering music for the digital age.
Here are the primary focus areas:
Enabling music for the disabled: For people with disabilities, technology has the potential to unlock new possibilities. Technology can enable communication, navigation, and independence of disabled people while learning and creating music.
Anti-piracy: According to Woolley, about 12.5 billion dollars are lost due to file sharing and music piracy, and 5 billion of that is profits lost from the music industry directly every year. Innovative technology can minimize and discourage music piracy.
Improving music recommendations: With the advent of technology, the glory of Radio DJs has passed, replacing musical gatekeepers with personalized algorithms and unlimited streaming services. With listeners now interested in a very diverse genre of music, content recommendation is at the heart of most subscription-based streaming platforms to enhance user experience and increase user engagement.
Ease of learning and playing music: New innovations provide fun and creative ways to enhance the learning experience. Apps and online tools can ease the more unsavory aspects of learning an instrument through gamification and progress tracking which help the learner stay motivated.
Innovate (reengineering music for the digital age): Innovate solutions that can make a difference in the world of music. You are only limited by your own imagination of what you can create.
We wish to bring together creative developer, designers, musicians, and product visionaries to test ideas and create products with the potential to change the world of music. There’s a lot that can be done here, so let’s unpack those beautiful ideas.Also, in addition to being good for humanity, this also helps foster innovation.
Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.
Artificial Intelligence (AI) has permeated virtually every industry, transforming operations and interactions. The tech recruitment sector is no exception, and AI’s influence shapes the hiring processes in revolutionary ways. From leveraging AI-powered chatbots for preliminary candidate screenings to deploying machine learning algorithms for efficient resume parsing, AI leaves an indelible mark on tech hiring practices.
Yet, amidst these promising advancements, we must acknowledge the other side of the coin: AI’s potential malpractices, including the likelihood of cheating on assessments, issues around data privacy, and the risk of bias against minority groups.
The dark side of AI in tech recruitment
The introduction of AI in recruitment, while presenting significant opportunities, also brings with it certain drawbacks and vulnerabilities. Sophisticated technologies could enable candidates to cheat on assessments, misrepresent abilities and potential hiring mistakes. This could lead to hiring candidates with falsifying skills or qualifications, which can cause a series of negative effects like:
Reduced work quality: The work output might be sub-par if a candidate doesn’t genuinely possess the abilities they claimed to have.
Team disruptions: Other team members may have to pick up the slack, leading to resentment and decreased morale.
Rehiring costs: You might have to let go of such hires, resulting in additional costs for replacement.
Data privacy is another critical concern
Your company could be left exposed to significant risks if your AI recruiting software is not robust enough to protect sensitive employee information. The implications for an organization with insufficient data security could be severe such as:
Reputational damage: Breaches of sensitive employee data can damage your company’s reputation, making it harder to attract clients and talented employees in the future.
Legal consequences: Depending on the jurisdiction, you could face legal penalties, including hefty fines, for failing to protect sensitive data adequately.
Loss of trust: A data breach could undermine employee trust in your organization, leading to decreased morale and productivity.
Financial costs: Besides potential legal penalties, companies could also face direct financial losses from a data breach, including the costs of investigation, recovery, and measures to prevent future breaches.
Operational disruption: Depending on the extent of the breach, normal business operations could be disrupted, causing additional financial losses and damage to the organization’s reputation.
Let’s talk about the potential for bias in AI recruiting software
Perhaps the most critical issue of all is the potential for unconscious bias. The potential for bias in AI recruiting software stems from the fact that these systems learn from the data they are trained on. If the training data contains biases – for example, if it reflects a history of preferentially hiring individuals of a certain age, gender, or ethnicity – the AI system can learn and replicate these biases.
Even with unbiased data, if the AI’s algorithms are not designed to account for bias, they can inadvertently create it. For instance, a hiring algorithm that prioritizes candidates with more years of experience may inadvertently discriminate against younger candidates or those who have taken career breaks, such as for child-rearing or health reasons.
This replication and possible amplification of human prejudices can result in discriminatory hiring practices. If your organization’s AI-enabled hiring system is found to be biased, you could face legal action, fines, and penalties. Diversity is proven to enhance creativity, problem-solving, and decision-making. In contrast, bias in hiring can lead to a homogenous workforce, so its absence would likely result in a less innovative and less competitive organization.
When used correctly, AI in recruitment can take your hiring to the next level
How do you evaluate the appropriateness of using AI in hiring for your organization? Here are some strategies for navigating the AI revolution in HR. These steps include building support for AI adoption, identifying HR functions that can be integrated with AI, avoiding potential pitfalls of AI use in HR, collaborating with IT leaders, and so on.
Despite certain challenges, AI can significantly enhance tech recruitment processes when used effectively. AI-based recruitment tools can automate many manual recruiting tasks, such as resume screening and interview scheduling, freeing up time for recruiters to focus on more complex tasks. Furthermore, AI can improve the candidate’s experience by providing quick responses and personalized communications. The outcome is a more efficient, candidate-friendly process, which could lead to higher-quality hires.
Let’s look at several transformational possibilities chatbots can bring to human capital management for candidates and hiring teams. This includes automation and simplifying various tasks across domains such as recruiting, onboarding, core HR, absence management, benefits, performance management, and employee self-service resulting in the following:
For recruiters:
Improved efficiency and productivity: Chatbots can handle routine tasks like responding to common inquiries or arranging interviews. Thereby, providing you with more time to concentrate on tasks of strategic importance.
Enhanced candidate experience: With their ability to provide immediate responses, chatbots can make the application process more engaging and user-friendly.
Data and insights: Chatbots can collect and analyze data from your interactions with candidates. And provide valuable insights into candidate preferences and behavior.
Improved compliance: By consistently following predefined rules and guidelines, chatbots can help ensure that hiring processes are fair and compliant with relevant laws and regulations.
Cost saving: By automating routine tasks for recruiters, chatbots can help reduce the labor costs associated with hiring.
Additionally, candidates can leverage these AI-powered chatbots in a dialog flow manner to carry out various tasks. These tasks include the following:
Personalized greetings: By using a candidate’s name and other personal information, chatbots can create a friendly, personalized experience.
Job search: They can help candidates search for jobs based on specific criteria.
Create a candidate profile: These AI-powered chatbots can guide candidates through the process of creating a profile. Thus, making it easier for them to apply for jobs.
Upload resume: Chatbots can instruct candidates on uploading their resume, eliminating potential confusion.
Apply for a job: They can streamline the application process, making it easier and faster for candidates to apply for jobs.
Check application status: Chatbots can provide real-time updates on a candidate’s application status.
Schedule interviews: They can match candidate and interviewer availability to schedule interviews, simplifying the process.
For hiring managers:
These can also be utilized by your tech hiring teams for various purposes, such as:
Create requisition: Chatbots can guide hiring managers through the process of creating a job requisition.
Create offers: They can assist in generating job offers, ensuring all necessary information is included.
Access requisition and offers: Using chatbots can provide hiring managers with easy access to job requisitions and offers.
Check on onboarding tasks: Chatbots can help track onboarding tasks, ensuring nothing is missed.
Other AI recruiting technologies can also enhance the hiring process for candidates and hiring teams in the following ways:
For candidates:
Tailor-made resumes and cover letters using generative AI:Generative AI can help candidates create custom resumes and cover letters, increasing their chances of standing out.
Simplifying the application process: AI-powered recruiting tools can simplify the application process, allowing candidates to apply for jobs with just a few clicks.
Provide similar job recommendations: AI can analyze candidates’ skills, experiences, and preferences to recommend similar jobs they might be interested in.
For recruiters:
Find the best candidate: AI algorithms can analyze large amounts of data to help you identify the candidates most likely to succeed in a given role.
Extract key skills from candidate job applications: Save a significant amount of time and effort by using AI-based recruiting software to quickly analyze job applications to identify key skills, thereby, speeding up the screening process.
Take feedback from rejected candidates & share similar job recommendations: AI can collect feedback from rejected candidates for you to improve future hiring processes and recommend other suitable roles to the candidate.
These enhancements not only streamline the hiring process but also improve the quality of hires, reduce hiring biases, and improve the experience for everyone involved. The use of AI in hiring can indeed take it to the next level.
Where is AI in recruitment headed?
AI can dramatically reshape the recruitment landscape with the following key advancements:
1. Blockchain-based background verification:
Blockchain technology, renowned for its secure, transparent, and immutable nature, can revolutionize background checks. This process which can take anywhere from between a day to several weeks today for a single recruiter to do can be completed within a few clicks resulting in:
Streamlined screening process: Blockchain can store, manage, and share candidates’ credentials and work histories. Thereby speeding up the verification and screening process. This approach eliminates the need for manual background checks. And leads to freeing up a good amount of time for you to focus on more important tasks.
Enhanced trust and transparency: With blockchain, candidates, and employers can trust the validity of the information shared due to the nature of the technology. The cryptographic protection of blockchain ensures the data is tamper-proof, and decentralization provides transparency.
Improved data accuracy and reliability: Since the blockchain ledger is immutable, it enhances the accuracy and reliability of the data stored. This can minimize the risks associated with false information on candidates’ resumes.
Faster onboarding: A swift and reliable verification process means candidates can be onboarded more quickly. Thereby, improving the candidate experience and reducing the time-to-hire.
Expanded talent pool: With blockchain, it’s easier and quicker to verify the credentials of candidates globally, thereby widening the potential talent pool.
2. Immersive experiences using virtual reality (VR):
VR can provide immersive experiences that enhance various aspects of the tech recruitment process:
Interactive job previews: VR can allow potential candidates to virtually “experience” a day i.e., life at your company. This provides a more accurate and engaging job preview than traditional job descriptions.
Virtual interviews and assessments: You can use VR to conduct virtual interviews or assessments. You can also evaluate candidates in a more interactive and immersive setting. This can be particularly useful for roles that require specific spatial or technical skills.
Virtual onboarding programs: New hires can take a virtual tour of the office, meet their colleagues, and get acquainted with their tasks, all before their first day. This can significantly enhance the onboarding experience and help new hires feel more prepared.
Immersive learning experiences: VR can provide realistic, immersive learning experiences for job-specific training or to enhance soft skills. These could be used during the recruitment process or for ongoing employee development.
To summarize, AI in recruitment is a double-edged sword, carrying both promise and potential problems. The key lies in how recruiters use this technology, leveraging its benefits while vigilantly managing its risks. AI isn’t likely to replace recruiters or HR teams in the near future. Instead, you should leverage this tool to positively impact the entire hiring lifecycle.
With the right balance and careful management, AI can streamline hiring processes. It can create better candidate experiences, and ultimately lead to better recruitment decisions. Recruiters should continually experiment with and explore generative AI. To devise creative solutions, resulting in more successful hiring and the perfect fit for every open role.
“Every hire is an investment for a company. A good hire will give you a higher ROI; if it is a bad hire, it will cost you a lot of time and money.”
Especially in tech hiring!
An effective tech recruitment process helps you attract the best talents, reduce hiring costs, and enhance company culture and reputation.
Businesses increasingly depend on technical knowledge to compete in today’s fast-paced, technologically driven world. Online platforms that provide technical recruiting solutions have popped up to assist companies in finding and employing top talent in response to this demand.
The two most well-known platforms in this field are HackerEarth and Mettl. To help businesses make wise choices for their technical employment requirements, we will compare these two platforms’ features, benefits, and limitations in this article.
This comparison of Mettl alternative, HackerEarth and Mettl itself, will offer helpful information to help you make the best decision, whether you’re a small company trying to expand your tech staff or a massive organization needing a simplified recruiting process.
HackerEarth
HackerEarth is based in San Francisco, USA, and offers enterprise software to aid companies with technical recruitment. Its services include remote video interviewing and technical skill assessments that are commonly used by organizations.
HackerEarth also provides a platform for developers to participate in coding challenges and hackathons. In addition, it provides tools for technical hiring such as coding tests, online interviews, and applicant management features. The hiring solutions provided by HackerEarth aid companies assess potential employees’ technical aptitude and select the best applicants for their specialized positions.
Mettl
Mettl, on the other hand, offers a range of assessment solutions for various industries, including IT, banking, healthcare, and retail. It provides online tests for coding, linguistic ability, and cognitive skills. The tests offered by Mettl assist employers find the best applicants for open positions and make data-driven recruiting choices. Additionally, Mettl provides solutions for personnel management and staff training and development.
Why should you go for HackerEarth over Mercer Mettl?
Because HackerEarth makes technical recruiting easy and fast, you must consider HackerEarth for technical competence evaluations and remote video interviews. It goes above and beyond to provide you with a full range of functions and guarantee the effectiveness of the questions in the database. Moreover, it is user-friendly and offers fantastic testing opportunities.
The coding assessments by HackerEarth guarantee the lowest time consumption and maximum efficiency. It provides a question bank of more than 17,000 coding-related questions and automated test development so that you can choose test questions as per the job role.
As a tech recruiter, you may need a clear understanding of a candidate’s skills. With HackerEarth’s code replay capability and insight-rich reporting on a developer’s performance, you can hire the right resource for your company.
Additionally, HackerEarth provides a more in-depth examination of your recruiting process so you can continuously enhance your coding exams and develop a hiring procedure that leads the industry.
HackerEarth and Mercer Mettl are the two well-known online tech assessment platforms that provide tools for managing and performing online examinations. We will examine the major areas where HackerEarth outperforms Mettl, thereby proving to be a great alternative to Mettl, in this comparison.
HackerEarth believes in upgrading itself and providing the most effortless navigation and solutions to recruiters and candidates.
HackerEarth provides various tools and capabilities to create and administer online tests, such as programming tests, multiple-choice questions, coding challenges, and more. The software also has remote proctoring, automatic evaluation, and plagiarism detection tools (like detecting the use of ChatGPT in coding assessments). On the other side, Mettl offers comparable functionality but has restricted capabilities for coding challenges and evaluations.
Test creation and administration
HackerEarth: It has a user-friendly interface that is simple to use and navigate. It makes it easy for recruiters to handle evaluations without zero technical know-how. The HackerEarth coding platform is also quite flexible and offers a variety of pre-built exams, including coding tests, aptitude tests, and domain-specific examinations. It has a rich library of 17,000+ questions across 900+ skills, which is fully accessible by the hiring team. Additionally, it allows you to create custom questions yourself or use the available question libraries.
Mettl: It can be challenging for a hiring manager to use Mettl efficiently since Mettl provides limited assessment and question libraries. Also, their team creates the test for them rather than giving access to hiring managers. This results in a higher turnaround time and reduces test customization possibilities since the request has to go back to the team, they have to make the changes, and so forth.
Reporting and analytics
HackerEarth: You may assess applicant performance and pinpoint areas for improvement with the help of HackerEarth’s full reporting and analytics tools. Its personalized dashboards, visualizations, and data exports simplify evaluating assessment results and real-time insights.
Most importantly, HackerEarth includes code quality scores in candidate performance reports, which lets you get a deeper insight into a candidate’s capabilities and make the correct hiring decision. Additionally, HackerEarth provides a health score index for each question in the library to help you add more accuracy to your assessments. The health score is based on parameters like degree of difficulty, choice of the programming language used, number of attempts over the past year, and so on.
Mettl: Mettl online assessment tool provides reporting and analytics. However, there may be only a few customization choices available. Also, Mettle does not provide code quality assurance which means hiring managers have to check the whole code manually. There is no option to leverage question-based analytics and Mettl does not include a health score index for its question library.
Adopting this platform may be challenging if you want highly customized reporting and analytics solutions.
HackerEarth: The security and privacy of user data are top priorities at HackerEarth. The platform protects data in transit and at rest using industry-standard encryption. Additionally, all user data is kept in secure, constantly monitored data centers with stringent access controls.
Along with these security measures, HackerEarth also provides IP limitations, role-based access controls, and multi-factor authentication. These features ensure that all activity is recorded and audited and that only authorized users can access sensitive data.
HackerEarth complies with several data privacy laws, such as GDPR and CCPA. The protection of candidate data is ensured by this compliance, which also enables businesses to fulfill their legal and regulatory responsibilities.
Mettl: The security and data privacy features of Mettl might not be as strong as those of HackerEarth. The platform does not provide the same selection of security measures, such as IP limitations or multi-factor authentication. Although the business asserts that it complies with GDPR and other laws, it cannot offer the same amount of accountability and transparency as other platforms.
Even though both HackerEarth and Mettl include security and data privacy measures, the Mettle alternative, HackerEarth’s platform is made to be more thorough, open, and legal. By doing this, businesses can better guarantee candidate data’s security and ability to fulfill legal and regulatory requirements.
Pricing and support
HackerEarth: To meet the demands of businesses of all sizes, HackerEarth offers a variety of customizable pricing options. The platform provides yearly and multi-year contracts in addition to a pay-as-you-go basis. You can select the price plan that best suits their demands regarding employment and budget.
HackerEarth offers chat customer support around the clock. The platform also provides a thorough knowledge base and documentation to assist users in getting started and troubleshooting problems.
Mettl: The lack of price information on Mettl’s website might make it challenging for businesses to decide whether the platform fits their budget. The organization also does not have a pay-as-you-go option, which might be problematic.
Mettl offers phone and emails customer assistance. However, the business website lacks information on support availability or response times. This lack of transparency may be an issue if you need prompt and efficient help.
User experience
HackerEarth: The interface on HackerEarth is designed to be simple for both recruiters and job seekers. As a result of the platform’s numerous adjustable choices for test creation and administration, you may design exams specifically suited to a job role. Additionally, the platform provides a selection of question types and test templates, making it simple to build and take exams effectively.
In terms of the candidate experience, HackerEarth provides a user-friendly interface that makes navigating the testing procedure straightforward and intuitive for applicants. As a result of the platform’s real-time feedback and scoring, applicants may feel more motivated and engaged during the testing process. The platform also provides several customization choices, like branding and message, which may assist recruiters in giving prospects a more exciting and tailored experience.
Mettl: The platform is intended to have a steeper learning curve than others and be more technical. It makes it challenging to rapidly and effectively construct exams and can be difficult for applicants unfamiliar with the platform due to its complex interface.
Additionally, Mettl does not provide real-time feedback or scoring, which might deter applicants from participating and being motivated by the testing process.
According to G2, HackerEarth and Mettl have 4.4 reviews out of 5. Users have also applauded HackerEarth’s customer service. Many agree that the staff members are friendly and quick to respond to any problems or queries. Overall, customer evaluations and feedback for HackerEarth point to the platform as simple to use. Both recruiters and applicants find it efficient.
Mettl has received mixed reviews from users, with some praising the platform for its features and functionality and others expressing frustration with its complex and technical interface.
May the best “brand” win!
Recruiting and selecting the ideal candidate demands a significant investment of time, attention, and effort.
This is where tech recruiting platforms like HackerEarth and Mettl have got you covered. They help streamline the whole process.Both HackerEarth and Mettl provide a wide variety of advanced features and capabilities for tech hiring.
We think HackerEarth is the superior choice. Especially, when contrasting the two platforms in terms of their salient characteristics and functioning. But, we may be biased!
So don’t take our word for it. Sign up for a free trial and check out HackerEarth’s offerings for yourself!
Let’s face it—cheating on tests is quite common. While technology has made a lot of things easier in tech recruiting, it
has also left the field wide open to malpractice. A 2020 report by ICAI shows
that 32% of undergraduate students have cheated in some form on an online test.
It’s human nature to want to bend
the rules a little bit. Which begs the question, how do you stay on top of cheating, plagiarism, and other forms of
malpractice during the assessment process?
How do you ensure that take-home assessments and remote interviews
stay authentic and credible? By relying on enhanced virtual supervision, of course!
HackerEarth Assessments has
always been one step ahead when it comes to remote proctoring which is able to capture the nuances of candidate
plagiarism. The recent advancements in technology (think generative AI) needed
more robust proctoring features, so we went ahead and built The HackerEarth Smart
Browser to ensure our assessments remain as foolproof as ever.
Presenting to you, the
latest HackerEarth proctoring fix - The Smart Browser
Our Smart Browser is the chocolatey version of a plain
donut when compared to a regular web browser. It is extra effective and comes packed with additional remote proctoring capabilities to
increase the quality of your screening assessments.
The chances of a candidate cheating on a HackerEarth technical assessment are virtually zero with
the latest features! Spilling all our secrets to show you why -
1. Sealed-off testing environment makes proctoring
simpler
To get started with
using the Smart Browser, enable the Smart Browser setting as shown above. This setting is available under the test
proctoring section on the test overview page.
As you can see, several other proctoring settings such as disabling
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automatically.Now, every candidate you invite to take the assessment will only be able to do so through the Smart
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candidate needs to click on the ‘start test’ button on the launch test screen, it opens in the Smart Browser. The
browser also prompts the candidate to switch to full-screen mode. Now, all candidates need to do is sign in and attempt
the test, as usual.
Also read: 6
Ways Candidates Try To Outsmart A Remote Proctored Assessment
2. Eagle-eyed online test monitoring leaves no room for error
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Any candidate actions attempting to switch tabs with the intent to copy-paste or use a generative AI like ChatGPT
are shown a warning and captured in the candidate report.HackerEarth’s latest proctoring fixes
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Also read: 4 Ways
HackerEarth Flags The Use Of ChatGPT In Tech Hiring Assessments
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have.
In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.
What is Headhunting in recruitment?
In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.
How do headhunting and traditional recruitment differ from each other?
Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.
In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.
AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams
All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.
Types of headhunting in recruitment
Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.
Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.
How does headhunting work?
Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.
Identifying the role
Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.
Defining the job
Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.
Candidate identification and sourcing
Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.
Approaching candidates
Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.
Assessment and Evaluation
In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.
Interviews and negotiations
Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.
Finalizing the hire
Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.
The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.
Common challenges in headhunting
Despite its advantages, headhunting also presents certain challenges:
Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.
Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.
Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.
Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.
Advantages of Headhunting
Headhunting offers several advantages over traditional recruitment methods:
Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.
Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.
Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.
Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.
Conclusion
Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.
The job industry is not the same as it was 30 years ago. Progresses in AI and automation have created a new work culture that demands highly skilled professionals who drive innovation and work efficiently. This has led to an increase in the number of companies reaching out to external sources of recruitment for hiring talent. Over the years, we have seen several job aggregators optimise their algorithms to suit the rising demand for talent in the market and new players entering the talent acquisition industry. This article will tell you all about how external sources of recruitment help companies scout some of the best candidates in the industry, the importance of external recruitment in organizations across the globe and how it can be leveraged to find talent effectively.
Understanding external sources of recruitment
External sources refer to recruitment agencies, online job portals, job fairs, professional associations and any other organizations that facilitate seamless recruitment. When companies employ external recruitment sources, they access a wider pool of talent which helps them find the right candidates much faster than hiring people in-house. They save both time and effort in the recruitment process.
Online job portals
Online resume aggregators like LinkedIn, Naukri, Indeed, Shine, etc. contain a large database of prospective candidates. With the advent of AI, online external sources of recruitment have optimised their algorithms to show the right jobs to the right candidates. Once companies figure out how to utilise job portals for recruitment, they can expedite their hiring process efficiently.
Social Media
Ours is a generation that thrives on social media. To boost my IG presence, I have explored various strategies, from getting paid Instagram users to optimizing post timing and engaging with my audience consistently. Platforms like FB an IG have been optimized to serve job seekers and recruiters alike. The algorithms of social media platforms like Facebook and Instagram have been optimised to serve job seekers and recruiters alike. Leveraging them to post well-placed ads for job listings is another way to implement external sources of recruitment strategies.
Employee Referrals
Referrals are another great external source of recruitment for hiring teams. Encouraging employees to refer their friends and acquaintances for vacancies enables companies to access highly skilled candidates faster.
Campus Recruitment
Hiring freshers from campus allows companies to train and harness new talent. Campus recruitment drives are a great external recruitment resource where hiring managers can expedite the hiring process by conducting screening processes in short periods.
Recruitment Agencies
Companies who are looking to fill specific positions with highly skilled and experienced candidates approach external recruitment agencies or executive headhunters to do so. These agencies are well-equipped to look for suitable candidates and they also undertake the task of identifying, screening and recruiting such people.
Job Fairs
This is a win-win situation for job seekers and hiring teams. Job fairs allow potential candidates to understand how specific companies work while allowing hiring managers to scout for potential candidates and proceed with the hiring process if possible.
Importance of External Recruitment
The role of recruitment agencies in talent acquisition is of paramount importance. They possess the necessary resources to help companies find the right candidates and facilitate a seamless hiring process through their internal system. Here is how external sources of recruitment benefit companies.
Diversity of Skill Sets
External recruitment resources are a great way for companies to hire candidates with diverse professional backgrounds. They possess industry-relevant skills which can be put to good use in this highly competitive market.
Fresh Perspectives
Candidates hired through external recruitment resources come from varied backgrounds. This helps them drive innovation and run things a little differently, thus bringing in a fresh approach to any project they undertake.
Access to Specialized Talent
Companies cannot hire anyone to fill critical roles that require highly qualified executives. This task is assigned to executive headhunters who specialize in identifying and screening high-calibre candidates with the right amount of industry experience. Huge conglomerates and companies seek special talent through external recruiters who have carved a niche for themselves.
Now that you have learnt the different ways in which leveraging external sources of recruitment benefits companies, let’s take a look at some of the best practices of external recruitment to understand how to effectively use their resources.
Best Practices for Effective External Recruitment
Identifying, reaching out to and screening the right candidates requires a robust working system. Every system works efficiently if a few best practices are implemented. For example, hiring through social media platforms requires companies to provide details about their working environment, how the job is relevant to their audience and well-positioned advertisements. The same applies to the other external sources of recruitment. Here is how you can optimise the system to ensure an effective recruitment process.
Craft Clear and Compelling Job Descriptions
Detail Responsibilities: Clearly outline the key responsibilities and expectations for the role.
Highlight Company Culture: Include information about the company’s mission, values, and growth opportunities to attract candidates who align with your organizational culture.
Leverage Multiple Recruitment Channels
Diversify Sources: Use a mix of job boards, social media platforms, recruitment agencies, and networking events to maximize reach. Relying on a single source can limit your candidate pool.
Utilize Industry-Specific Platforms: In addition to general job boards, consider niche job sites that cater to specific industries or skill sets
Streamline the Application Process
Simplify Applications: Ensure that the application process is user-friendly. Lengthy or complicated forms can deter potential candidates from applying.
Mobile Optimization: Many candidates use mobile devices to apply for jobs, so ensure your application process is mobile-friendly.
Engage in Proactive Sourcing
Reach Out to Passive Candidates: Actively seek out candidates who may not be actively looking for a job but could be a great fit for your organization. Use LinkedIn and other professional networks for this purpose.
Maintain a Talent Pool: Keep a database of previous applicants and strong candidates for future openings, allowing you to reach out when new roles become available.
Utilize Social Media Effectively
Promote Job Openings: Use social media platforms like LinkedIn, Facebook, and Twitter to share job postings and engage with potential candidates. This approach can also enhance your employer brand
Conduct Background Checks: There are several ways of learning about potential candidates. Checking out candidate profiles on job boards like LinkedIn or social media platforms can give companies a better understanding of their potential candidates, thus confirming whether they are the right fit for the organization.
Implement Data-Driven Recruitment
Analyze Recruitment Metrics: Track key metrics such as time-to-hire, cost-per-hire, and source effectiveness. This data can help refine your recruitment strategies over time. Using external hiring software like HackeEarth can streamline the recruitment process, thus ensuring quality hires without having to indulge internal resources for the same.
Use Predictive Analytics: In this age of fast paced internet, everybody makes data-driven decisions. Using predictive analytics to study employee data will help companies predict future trends, thus facilitating a productive hiring process.
Conclusion
External sources of recruitment play a very important role in an organization’s talent acquisition strategy. By employing various channels of recruitment such as social media, employee referrals and campus recruitment drives, companies can effectively carry out their hiring processes. AI-based recruitment management systems also help in the process. Implementing best practices in external recruitment will enable organizations to enhance their hiring processes effectively while meeting their strategic goals.
Recruiters constantly look for innovative ways and solutions to efficiently attract and
engage top talent. One of the recruiter tools at their
disposal is the recruitment chatbot. These digital assistants are revolutionizing how recruiters work.
Are you looking to add a chatbot to your hiring process?
Our comprehensive guide will take you through the essentials of a recruitment
chatbot-from its role and benefits to planning and building one and optimizing your own.
The rise of AI in recruitment
Artificial intelligence (AI) is a transformative force reshaping most industries, if
not all. Today, you'll find AI-generated marketing content, financial predictions, and even AI-powered contact center solutions. The recruitment field has not been left behind. Professionals are using AI technologies,
such as machine learning, natural language processing (NLP), and predictive analytics, to enhance various aspects
of recruitment.
A report by Facts & Factors projects the global AI recruitment market size will grow
to $890.51 million by
2028. Source
Chatbots are a prime example of AI's practical application in the hiring process.
They efficiently handle tasks that traditionally require constant human intervention-as we'll see in the next
section.
Understanding recruitment chatbots
Now that you understand the role of AI in modern recruiting processes, let's focus on
recruitment chatbots in particular.
What is a recruitment chatbot?
A recruitment chatbot is software designed to assist in the recruitment process by
simulating human-like conversations and automating various tasks. The core functionalities include:
Asking candidates predefined questions about their
qualifications, experience, and skills
Instantly responding to common questions about job
openings, company culture, benefits, and application process
Automated interview scheduling process with human
recruiters
Keeping qualified candidates informed about their
application status
As of 2023, 35%-45% of companies were using AI recruitment tools. Here are two key
notable ones:
General Motors
General Motors (GM) has a conversational hiring assistant, Ev-e, that appears as soon
as you land on their career site. Source
This AI-powered chatbot enabled GM to manage candidate communications efficiently.
The company also lowered its interview scheduling time from 5-7 days to just 29 minutes. They also save around $2 million annually.
Hewlett Packard Enterprise
Hewlett Packard Enterprise (HPE) also has a great recruiting chatbot- the HPE Career
Bot. It also pops up when you land on HPE's career site. Source
HPE's goal was to use the chatbot to convert passive candidates into actual job
applicants, and they did just that.
Within the first three months of its rollout, the career bot more than doubled its
usual career site visitors, reaching over 950,000 candidates. Additionally, HPE converted 26% of job seekers into
actual hires.
Benefits of using recruitment chatbots
>
The key benefits of using a recruitment chatbot include:
Saving valuable time: Recruitment chatbots can
automate repetitive tasks like answering FAQs. That speeds up the recruitment process, allowing
recruiters to focus on other administrative tasks.
24/7 availability: Unlike human recruiters,
who can only work 9-10 hours daily, chatbots are available around the clock.
Better quality of hires: Chatbots use
predetermined criteria for the initial candidate screening process, meaning they only approve
qualified candidates.
Lower hiring costs: By automating various
time-consuming tasks, chatbots help significantly reduce recruitment costs.
By doing all the above, recruitment chatbots help you save resources that would be
unnecessarily wasted if you were using the traditional hiring process.
Planning your recruitment chatbot
Without a well-thought-out plan, even the most advanced chatbot will fall short of
expectations.
Defining your chatbot's objectives
Before building your recruitment chatbot, clearly understand what you want to achieve
with it. Setting specific objectives. Some objective examples are:
To screen applicants
To schedule interviews
To provide company information
To identify the ideal objectives for your recruitment chatbot, map out the candidate
journey from their initial interaction to the final hiring decision. Then, identify the touchpoints where the
chatbot can add value.
For instance, if you waste most of your time screening candidates,
create a chatbot that can efficiently assess qualifications and experience.
Establish metrics to measure chatbot success. They should align with the goals you
set. Some great metrics could be a reduction in time-to-hire or candidate satisfaction scores.
Designing conversations for optimal engagement
The next step is to design the conversations your chatbot might have with candidates.
Cover everything from greetings to solutions to misunderstood queries.
Greetings: Always begin with a warm greeting.
Language: Avoid jargon and overly formal
language. Use simple, straightforward, conversational language.
Guided approach: Steer the conversation,
providing clear instructions. You can also include quick reply buttons for common responses.
Misunderstood queries: Ensure your chatbot
handles misunderstandings gracefully by politely asking for clarification.
Don't forget to include options for the chatbot to escalate complex queries to a human
recruiter.
Building your recruitment chatbot
Now, you're ready to build a recruitment chatbot that will improve your overall
talent acquisition strategy.
Choosing the right platform
Start by choosing the right chatbot platform. For this, there are factors you must
consider.
The first is whether it will help you build a chatbot that meets your needs. To
determine this, refer to your objectives. For instance, if your objective is to reduce repetitive inquiries,
ensure the platform has strong NLP capabilities to understand and respond to candidate queries naturally.
The other factor is your technical expertise. Determine whether you need a
no-code/low-code platform or have the technical resources to build a custom solution.
The no-code or low-code solution with pre-built templates is ideal for recruitment
teams without extensive technical expertise. The custom solution, on the other hand, suits teams with technical
resources.
Besides that, consider the features each chatbot tool offers. For instance, does it
have multi-channel support, customization options, integration capabilities, and detailed analytics? Also, ensure
you choose an option within your budget.
Some popular chatbot platforms include Mya, Olivia, XOR, and Ideal.
Development and integration
Developing and integrating your recruitment chatbot is the next. Here's a step-by-step
guide:
Define the scope and workflows: Identify the
ideal candidate touchpoints-where and how the chatbot will interact with potential candidates.
Scriptwriting: Write scripts for possible
interactions the chatbot will have with candidates. Use generative AI tools to generate great responses that align with your desired
conversation tone and style in minutes.
Build the chatbot: Use your chosen platform to
build a chatbot that aligns with your workflow and scripts.
Testing: Conduct thorough testing to identify
and fix any issues. You can start with your team and then beta-test it with a small group of
suitable candidates.
Integrate with existing HR systems: Integrate
your recruitment chatbot with your Applicant Tracking System (ATS), your calendar, among others.
Once you're confident in the chatbot's performance, roll it out to candidates.
Training and optimizing your chatbot
Continuously train and optimize your recruitment chatbot to keep it aligned with your
goals, changing recruitment needs, and company policies. Let's break this down:
Training your chatbot with AI and Machine Learning
Start by collecting historical data from past interactions, such as emails, chat logs,
and support tickets, to use as the initial training data set. Leverage the data to teach your chatbot how to
understand and respond to various candidate inquiries.
The data should include a wide range of scenarios.
Also, use NLP to train your recruitment chatbot to understand and process human
language. You can use NLP frameworks like AllenNLP, Apache OpenNLP, or Google's BERT.
Implement a continuous learning loop where your recruitment chatbot can learn from
new interactions to expand its knowledge base and adjust its conversational strategies.
Monitoring and improving chatbot performance
Regularly monitor your recruitment chatbot interactions and metrics to improve your
recruitment chatbot performance and ensure candidate satisfaction.
Constantly review your interaction logs to understand how candidates are interacting
with the chatbot. Identify common issues or misunderstandings. You can also collect user feedback directly from
candidates who have interacted with the chatbot.
Track metrics like response accuracy, conversation completion rate, candidate
satisfaction scores, and time saved for recruiters. You can then use the valuable insights to refine the scripts,
improve responses, and address the knowledge gaps.
Additionally, keep up with the latest trends and advancements in AI and recruitment
technology to maintain the chatbot's relevance over time.
Legal and ethical considerations
Using AI in recruitment comes with legal and ethical challenges. These
include:
Ensuring compliance and privacy
Ensure your chatbot complies with data protection laws and regulations to avoid
unnecessary legal suits.
Most regulations require you to inform candidates about the personal data collected,
how you will use it, and your data retention policy.
Popular regulations include the General Data Protection Regulation (GDPR), the
California Consumer Privacy Act (CCPA), and Canada's PIPEDA.
Addressing bias in AI
AI-driven recruitment tools can unknowingly carry on biases from the training data or
algorithms. You must address these biases to
ensure fair and equitable treatment of all candidates.
Use diverse and representative training data to reduce the risk of biased outcomes.
Also, regularly audit your training data for biases related to gender, race, age, disability, or other protected
characteristics.
Best practices and tips
Implementing a recruitment chatbot requires you to follow best practices to
effectively meet your hiring goals while providing a positive candidate experience.
Dos and don'ts for recruitment chatbots
Here are some of the most essential tips and common pitfalls:
Dos
-Ensure your chatbot is user-friendly and capable of handling various inquiries at a
go.
-Offer personalized experiences.
-Provide relevant and timely information.
-Ensure the chatbot is accessible to all candidates, including those with
disabilities.
Don'ts
-Don't over-automate. Maintain a balance with human touchpoints
-Don't overwhelm candidates with too much information at once
Future trends in AI recruitment
The future of AI in recruitment looks promising, with trends such as advanced natural
language processing (NLP). The advanced capabilities will allow chatbots to understand and respond to more
complex queries.
Besides that, we can expect future chatbots to use more interactive content, like
video intros, virtual reality (VR) job previews, or virtual workplace tours to boost candidate engagement. A
company like McKinsey & Company is already using gamified pre-employment assessments. Source
We will also see more advanced AI-powered candidate matching that provides
personalized job recommendations based on a candidate's skills, experience, and career aspirations.
Conclusion
Recruitment chatbots are revolutionizing the recruiting process. By automating
routine tasks, providing instant responses, and offering data-driven insights, chatbots enhance both recruiters'
and candidates' experiences.
As discussed in this guide, implementing a recruitment chatbot involves several
crucial steps.
Define the objectives and design conversation paths. Next, choose your ideal platform
and build your chatbot. After that, train and continuously optimize it to ensure it remains accurate and
relevant. Also, ensure you're complying with the core legal and ethical considerations.
Now go build a recruitment chatbot that slashes your workload and gives your
candidates a great experience.
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:
