Kumari Trishya

The last time college graduates faced an uncertain job future, it was 2008 and we were staring down at the Great Recession. This time, it’s worse.

Statistics suggest that the pandemic has left us with a worse labor market than 2008. While some companies like Google, Salesforce, and Amazon decided to forge ahead with remote internships despite the pandemic, there were many like L’Oreal, Indeed, Discovery Inc. and Buzzfeed who had to cancel offers. Hiring freeze was a commonly used term across industries last year.

Now, with the vaccine in play and normalcy looking achievable, there are signs that the freeze has begun to thaw. Monster’s recent Future Of Work report filled out by 3,100 respondents shows that tech hiring is on the rise. 49% of the respondents from the tech industry who took the survey said that they would either backfill existing roles or open up new ones in 2021.

So, that’s good news! But…

If you’re a tech recruiter hiring in 2021, you know that the playing field has changed. First up, you’re probably looking at a larger talent pool with more graduates vying for the same role than ever before. While a larger funnel is welcome, it also means that your assessment and interview process has to be streamlined and well structured.

Second, you are going to be dealing with altered expectations. The pandemic has changed the definitions of jargons we took for granted – job satisfaction, loyalty, empathy. Employees joining the workforce today expect different things than their predecessors. Our State of Developer Recruitment 2020 report shows that more developers were asking for benefits like health insurance, mental wellness days, and; most importantly, upskilling, before taking up an offer.

Then there’s the increased focus on skill-based, bias-free hiring practices. Tech hiring has been called out again and again for it’s “boys club” nature, which keeps women and minorities away from leadership roles. As recruiters, the onus is on us to make changes at the grassroot levels (i.e. when hiring early talent) and not give in to age-old practices.

Sounds overwhelming? Don’t worry, we got you!

We went around and asked our favorite recruiters to give us some tips on how to realign the campus hiring process for 2021 and beyond. Here’s what they told us:

1. Be honest about job requirements and the ideal candidate fit

A downturn can cause individual workloads to increase, and might expand other job roles. If you’re still being conscientious with your hiring budget and not doing a lot of lateral hiring, then you might need interns and early hires to take on more work than under normal circumstances. Considering the influx of candidates you might be dealing with, this is the time to be open about your requirements and your company culture.

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2. Hire for skills and ditch those resumes

The class of 2021 will have a gap year on their resumes. So did the class of 2008. Many of them went back to college to enroll in further studies, while others decided to use the time upskill and learn new skills.

Don’t look for the flaws in the resumes. Look for the skills that would make a developer fit in with your company’s changed business model and outlook. Somebody who invests in themselves and their skills is an asset during changing times, and you’ll be happy to have them on your side through tough times.

The right tools go a long way in helping you hire the best talent. Especially with campus hiring, where you’re dealing with a large talent pool, manual assessments do not make the cut. Instead, you should look to using platforms that let you send out standardized tests to multiple candidates, and choose the best. Even with interviews, make sure you’re using a platform that has real-time pair-programming and coding features so as to gauge skill levels better.

3. Talk about upskilling initiatives

On the subject of skills, let’s make it a two-way conversation, please. As important as it is for companies to hire skilled talent, the reverse is equally true, too. Developers at every level are looking for upskilling opportunities, and they would like their employers to understand this. In our annual recruiter survey, we found that 60.7% of our respondents said ‘Company Sponsored Upskilling’ was a must-have benefit for a tech employer. This is what prospective candidates were asking for during the interviews.

Studies show that 40% of Gen Z professionals prefer learning and growth opportunities over other benefits at the workplace. This is a generation that grew up on YouTube tutorials and podcasts, and had an entire gap year to do nothing but upskill. Do they still want more? Yes!

So, shine the light on your company’s L&D initiatives and get ready to have some fruitful conversations.

Further Reading

Learn more about how HackerEarth can help you upskill your team!

4. Promote your brand values

A Deloitte survey shows that Gen Z prefers to interact with and work for companies based on their ethics, practices, and social impact. Companies have to go beyond lip service. It’s important to validate your commitment towards society through action. These values, or “karma points”, can help you create a distinct brand identity among all the others vying for the top talent.

Having a clearly defined employer brand and promoting your core values and benefits also enables you to attract the best candidate fit right from the start, thereby reducing time-to-hire, increasing retention rates, and driving down additional costs of re-recruiting.

Gen Z is also attracted to brands that promote and foster diversity as we found out. Companies like Salesforce with their equality and inclusion programs have changed the dialogue around D&I, and promoting your company’s efforts will only help you be a part of an important conversation.

5. Get your remote onboarding process organized

This one is a no-brainer, right? And yet, it matters quite a bit. Jamini Pulyadath; HackerEarth’s Senior Manager (Talent Acquisition), recently wrote about the ways in which recruiters can make remote onboarding easier for new employees, and you can adopt some of her tricks for your early hires as well.

You can also take a page out of our team member Ruehie’s book here. She recently wrote a piece aimed at helping recruiters create a meaningful and engaging virtual onboarding experience for interns and I think this might help you if you’re running an internship program.

Looking for remote hiring solutions? We got your back!

Bonus: Don’t forget to be empathetic

I recently read an article in the Harvard Business Review about handling grief in the workplace. What we have been through as a collective is unprecedented, and it’s hurt us all in different ways. The graduating class of 2021 lost the chance to start a life they had been dreaming of, and are worried about getting a late start to their careers. Economists call this phenomenon a ‘failure to launch’, and they say it can have a lasting impact on a generation.

Empathy is the least we can offer them, and it doesn’t come at a high cost. So, why not bake it into your early talent hiring process, eh?

*****

To know more about how HackerEarth can help you with your campus hiring requirements, do write to us at support@hackerearth.com.

It has been well established that you cannot hire a developer unless you see them work on a problem real-time.

For the last eight years, we have observed and figured out flaws in the way the tech world approaches the uber-important ‘interview round’. Let us list them for you:

1. Non-standardized, subjective evaluations due to lack of preparation time: From our conversations with multiple hiring managers and recruiters, we realized that ‘prep time’ is a misnomer. Most interviewers we talked to said they could only manage to start prepping for an interview 15 minutes before it started. A lot of the time, candidate resumes are checked minutes before the actual interview happens. With very little ‘preparation’, interview questions are chosen from a pool of common, generic questions or are made up on the spot. This makes the interview non-standardized as the evaluation parameters are not customized to the role at hand.

2. Time away from ‘core’ function: Finding the right questions to ask your candidate and setting up evaluation parameters is time taking. Add to that the time spent in writing manual feedback, and one can see that hiring managers spend a lot of their working hours taking interviews, and away from their core job function. Here’s a quick calculation of how much time your company loses with inefficient interviews:

‍3. The pain of writing detailed interview summaries: Many hiring managers we talked to said they inadvertently end up delaying their interview feedback at the very last minute due to time crunch. Summarizing an hour-long conversation into succinct points requires a razor-sharp memory and ample patience. When you’re rushing through this part of the process, staying objective is hard and hiring managers tend to use ‘gut feel’ in their feedback. Post-interview feedback in most cases is almost always broken, and the age-old problem of miscommunication between recruiters and hiring managers continues to persist due to the lack of an efficient process.

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We believe all coding interviews should be OBJECTIVE and SKILL-BASED. So, we decided to end these problems for good

With the enhanced version our coding interview platform – FaceCode – we have added to our suite of kickass developer-hiring tools to ensure that the developer-hiring life cycle remains objective and skill-based.

What’s FaceCode you ask? Only the most advanced coding interview tool ever with a host of features that weed out subjectivity and bias from every situation – whether you’re conducting interviews in person or remotely. FaceCode is like noise cancellation for your coding interviews. It cuts out all the unnecessary noise and allows hiring managers to focus on what matters most: SKILLS.

Here’s how FaceCode helps you solve the problems we listed above (and more):

a. Collaborative, real-time code editor with built-in compiler, and question library

Benefit: Assess design problems without navigating away from your interview interface.

System design problems are a necessity in most senior developer interviews. Our collaborative, real-time diagram board helps you evaluate this skill seamlessly for back-end, cloud engineer and other roles.

e. Diversity Hiring Mode - Hides PII

Benefit: Bye bye bias!

FaceCode interviews come with an anti-bias feature. As a hiring manager, if you would like to keep your interviews bias-free you can do so by switching this feature ‘ON’ when you schedule an interview. This hides the candidate’s PII (Personally Identifiable Information) and masks their name with an alias.

[caption id="attachment_28587" align="aligncenter" width="900"] Hide personal details with aliases[/caption]

And there you have it!

HackerEarth has a policy: Don’t Be An Asshole (it’s part of our cultural tenets). Which also means, we aren’t judgmental people. Unless it’s about how you conduct your technical coding interviews :P

A version of FaceCode with fewer features was made available to HackerEarth clients as an add-on to HackerEarth Assessments late in 2019. The platform saw a 4000% increase in the number of interviews conducted between Q3 2019 and Q3 2020. No doubt some of it was driven by the pandemic, but it is also testament to the fact that hiring managers are waking up to the benefits of having an automated coding interview tool that can help them save time and conduct flawless interviews both remotely, as well as in person.

Yes, many users have come back to us to say that they would continue to use FaceCode even in face-to-face interviews because of its time-saving features, and because how it makes interviews supremely objective. This is why we believe that FaceCode is THE definitive way to conduct coding interviews virtually or on-site!

We’ve heard a lot of good things about FaceCode from our users and we’re glad to be launching it publicly! If you are interested in getting a free trial, do write in to our product whizkid Akash Bhat (akash@hackerearth.com).

Join us as we say goodbye to subjective, inefficient, and outdated interview processes, yes? We’ll wait for you.

Problem:
There’s a port somewhere in the world that wants to maximize profits.

Approach:
Said port hires a data scientist to look at the numerous variables affecting ship movement and operational efficiency – factors that affect profitability in the long run.

The data scientist looks at how many ships enter the port on a daily basis, where they are loaded and unloaded, the size of ships coming in versus the length of the docks where they are anchored, the time lost when a ship of the wrong size enters a dock and then has to re-dock correctly, the number of port employees required to unload a single ship by length and type of cargo, the future plans for the port and the predicted volume of ships entering.

Then they begin their analysis.

Conclusion:
Our data hero announces that the port will have to hire at a rate of 3% every year to keep up with increasing volume. They also help the authorities set up a system that helps ships navigate to the correct dock and alerts authorities in advance when a ship is approaching. This leads to increased efficiency overall, better communication between the docks and the ships; thus decreasing time lost in re-docking, and increases profits for the port.

Accounting for seasonal variations in traffic, and the time and effort needed to train the staff in using the new navigation system, the data scientist predicts that the port can look at a probable profit increase of 20% in 3 years.

**The key word here is ‘probable’.**

Let’s read that first part again. The solution seems so simple, right? That simple solution, however, requires months of data crunching and historical analysis to create operational models for the future.

The end result in this scenario is a probability and not a number written in stone, because several factors (trade wars, a pandemic, oil prices, consumer demand) can affect the port’s operations. These are factors one cannot guarantee, or foresee, but a good data scientist is expected to account for all of these and still come up with a reliable prediction.

This is why good data scientists are so in-demand across the tech sector. Also, why assessing and hiring good data scientists is so hard.

Data scientists are not the same as generalist programmers

Assessing a data scientist is not the same as assessing another developer. The above example would have helped you understand the difference between the problems that a data scientist works on and those that a programmer solves.

There are differences even in the skill sets required for a data scientist role, and those required by other developers as illustrated below:

Traditional IDEs, therefore, don’t cut it for data scientists

Most IDEs include a source code editor, debugger, and compiler. They work perfectly for tech assessments for programmers and developers. Not for data science and machine learning assignments though.

In many data science problems, the solution can be a simple prediction or a ‘Yes/No’ answer. Or, if we go back to the question we started this blog with, it can be a prediction about the probability of achieving the desired goal. Is it going to rain in Atlanta tomorrow? Yes. Will my company grow 5X in the next two years? Ummm, there’s a 20% chance of doing that given you do these 10 other things well.

As we have already established, arriving at this answer requires hours of logical analysis. When assessing a data scientist for a job, therefore, recruiters and hiring managers need to be able to understand the logical choices the candidate made while arriving at the seemingly simple conclusion. A traditional IDE is not enough here.

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Hence, Jupyter Notebooks

At HackerEarth, we have seen an increasing demand for Data Science and Machine Learning – a trend reflected in our year-end recruiter survey as well. To make data science assessments easier for recruiters, we have now integrated Jupyter Notebooks on our assessment platform, which helps recruiters get right inside the mind of the candidate they are trying to hire.

The Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. The easy-to-use, interactive data science environment provided by Jupyter works across several programming languages such as Python and R. Jupyter Notebooks not only work like an IDE, but also as a presentation or education tool, and are great for data science assessments where the candidate is required to answer questions in a visual format.

Here are some of the ways Jupyter Notebooks score over traditional IDEs:

• Individual cells for better analysis

Jupyter Notebooks allow candidates to code using separate units or ‘cells’ that can be used independently of each other while writing code (denoted by red arrows in the image below). This makes it easier for candidates to compute how various data parameters work with each other and to add notes, or to partially write and test code.

This is essential for recruiters to understand the analytical approach taken by the candidate when solving a problem.

• Interactive elements for better data visualization

The Notebook offers an interactive shell with embeddable graphics and tables, reusable cells, and some other presentation features which are relevant to the job at hand. This enables candidates to present their output in a graphical format if needed; something that a traditional IDE does not support.

• Enhanced candidate experience

It is well known that candidates perform better when they are using a test environment they are familiar with. Notebooks are a preferred tool in the data science world. Using the Jupyter platform for an assessment ensures that your candidate is comfortable and ready, and is approaching a problem the way they would in real life.

Better data science assessments are made of these

When the candidate starts the assignment, they are given a choice to use the Monaco editor (IDE) or Jupyter Notebooks. The Notebooks use a dedicated machine to provide enough resources to each user. Thus by ensuring a dedicated machine for every assignment our candidates take, we affirm that the candidate has no restrictions and completely feels at home. This directly translates to better candidate output in the test, and an objective skill-based assessment process.

The most interesting bit about the Jupyter Notebook integration is the output section, which not only captures the final submission in CSV format but allows recruiters to review each and every step taken by the candidate as they solved the data problem before them.

So, even if a candidate gets a Yes/No prediction wrong, you can still review their work to see how they analyzed the data – the most crucial part of a data scientist’s role.

Find better candidates with Jupyter Notebooks. Thank us later!

While data science as a field dates back to 1962 when mathematician John W. Tukey predicted the effect of modern-day electronic computing on data analysis as an empirical science. However, it reached the modern-day tech hiring lexicon only in recent years.

The trends we have seen tell us that tech jobs in AI (Artificial Intelligence), ML (Machine Learning), and Data Science would be the most in-demand roles in the future. With growing opportunities for AI and ML specialists in industries as diverse as banking, fintech, public safety, and healthcare, there will be a surge in these roles in the coming days. Today, every business big or small needs BIG DATA, and with the advent of various technologies that allow easy application of data science, all businesses are looking at using data to make their solutions smarter, their operations more efficient, and their user experiences more personalized.

This predicted surge in hiring also underlines the need to objectively assess and hire the best data scientists in the market. Traditional modes of evaluation do not do justice to the skills and expectations associated with this role. With the Jupyter notebook support on our HackerEarth Assessments platform, however, you can now assess and hire the best data scientists out there, and improve your business pipeline.

Try it out and let us know? You can even mail our product manager Akash Bhat (akash@hackerearth.com) to know more about this feature.

Recommended Read: HackerEarth’s Complete Guide to Hiring A Data Scientist