Developer Insights

Top 7 most popular programming languages to learn

This blog has been picked up by the Economic Times Tech

Top Programming Languages to Learn

How do we determine the most preferred programming languages?

By “most preferred,” we don’t mean that Java is superior to C++ or Python is better than MATLAB. This analysis aims to highlight languages most in demand in the industry and most preferred by users.

Languages like C++, MATLAB, and Java are traditionally taught in technical institutes. However, industry requirements often differ from academic focus areas.

Languages to Learn:

Javascript
Java
Python
PHP
R
Matlab
Arduino
Swift

We pulled data from TIOBE, PYPL, Stack Overflow, GitHub, Indeed, Glassdoor, and our own platform at HackerEarth to identify which languages are preferred by developers and most sought after by companies.

TIOBE Index

TIOBE measures language popularity based on search engine queries. In 2016, Java remained #1, followed by C and C++. Visual Basic and Python scored higher than Javascript, and Assembly entered the top 10.

PYPL Index

PYPL ranks languages by how often tutorials are searched on Google. Java leads worldwide, while Python has grown rapidly in popularity. PHP has seen a decline.

Stack Overflow

Stack Overflow is one of the largest programming communities. Based on questions asked, Javascript tops the list, with Node.js and Angular gaining popularity while PHP declines.

GitHub

GitHub's Octoverse report showed Javascript as the most widely used language in repositories, outpacing other languages significantly.

HackerEarth

HackerEarth hosts thousands of coding challenges. Based on internal data from over 1 million users, the most used languages are C++, Java, Python, and C#.

Indeed

Indeed job listings show Java as the most in-demand, followed by PHP, C, and Javascript. R is also gaining traction.

Glassdoor

Glassdoor job data suggests Java remains top for job demand. Developers increasingly prefer R, while Python and Perl are rising in usage.

Recommended Programming Languages for 2017

Javascript: Essential for web development. Widely used for frontend interactions and effects.
Java: Backbone of Android apps and financial systems. Known for speed and performance.
R: Perfect for statistics and data analysis, with rising job market demand.
MATLAB: Regaining popularity due to analytics and scientific computing needs.
SQL: Crucial for database management. A must-have for data-focused roles.
Arduino: Vital for IoT and embedded systems, based on C/C++.
Swift: Apple's modern programming language for iOS/macOS app development.

These languages are expected to be in high demand in the near future. Others won’t vanish, but these are currently trending in the developer and employer communities.

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Developer Insights

Why study data structures and algorithms?

Many young programmers scour the web trying to find answers to this question: How to study Algorithm and Data structure? Certainly, a good place to start... But a more relevant question would be: What are algorithms and data structures, and why should I study them?

Let’s start with this motivating quote: “Every program depends on algorithms and data structures, but few programs depend on the invention of brand new ones.” -- Kernighan & Pike

What is an algorithm?

Wikipedia says “an algorithm is a self-contained step-by-step set of operations to be performed. Algorithms perform the calculation, data processing, and/or automated reasoning tasks.”

Whether you are cooking a burger or adding two numbers, there’s an algorithm at work. From monitoring stock markets to pairing soul mates, algorithms are omnipresent.

An algorithm is a detailed step-by-step instruction set or formula for solving a problem or completing a task. You need algorithms to achieve the input–output relationship. Repetition, sequencing and conditional logic are computational concepts that manifest in your everyday life. Your morning routine can be an algorithm. Can you guess how?

There’s this great TEDEd cartoon by Harvard computer scientist David J. Malan you should see.

Algorithms are much more than instructions. What matters is that they teach you to define clear steps and conceptualize solutions.

Examples, anyone?

If you're shopping for a prom dress or driving to the mall, you are using a greedy algorithm. You make the locally optimum choice at each stage hoping to find the global optimum.

If you know the famous traveling salesman problem, then you’ll know how it applies to optimizing delivery routes.

Read this article about a two-year project led by Prof. William Cook. His team calculated the shortest distance to nearly 25,000 pubs in the UK using Google Maps.

What approach would you use to find a name in a phonebook? The brute-force algorithm. Try all possibilities until you find a solution. It works, but not well for complex problems.

Remember when IBM’s Deep Blue used brute-force methods to defeat chess champion Garry Kasparov? Times have changed, and so has computational power.

Then there are data compression algorithms—like MP3, JPEG, ZIP—that reduce file size. Huffman coding is a greedy approach that works well for efficient encoding.

The PID algorithm uses feedback to control systems like robots or heating units. It's still widely used, despite newer alternatives like MPC in cutting-edge applications.

What is Data Structure and why study it?

Data structure refers to an orderly arrangement of data. Think of it like organizing documents in folders. From encyclopedias to bank statements, structure matters.

Think not binary trees or associative arrays; think a shopping list. Data structures store objects and allow their manipulation. A mathematical model of a data structure is an abstract data type (ADT). These can be linear (arrays, stacks, queues) or nonlinear (trees, graphs, sets).

Abstractions help us focus on the big picture. ADTs specify the fields and operations; data structures implement them. For instance, Java source code access is limited so programmers can’t tamper with the software core.

abstract data types

Now how do we connect algorithms to data structures?

You use an algorithm—like looking up synonyms in a thesaurus—using data from a structured source (the thesaurus). Algorithms use the organization provided by data structures. You can’t really split them. “Data structures organize data and algorithms use that organization.”

Understanding these high-level building blocks is essential for programming success.

"I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships." – Linus Torvalds

Learn more about how algorithms are taking over our world and how to get started with competitive programming.

Just to reiterate, you can't make a house by just stacking bricks—you need to understand the problem, create a feasible solution, and implement it without losing sight of the big picture.

Developer Insights

Gradient descent algorithm for linear regression

You are probably using machine learning multiple times a day without realizing it. For instance, when checking your mailbox, a spam filter automatically filters out junk mail—thanks to Machine Learning (ML). ML is the science of training a machine to learn from past data, without being explicitly programmed.

There are two main types of ML techniques:

Supervised Machine Learning: The system learns from predefined training data to predict future outcomes.
Unsupervised Learning: The system identifies hidden patterns in data without prior labels—for example, finding close friend groups on Facebook.

Supervised Learning

Consider the following dataset showing house prices in Bengaluru, India:

Living area (sq ft)	Price (USD)
820	30105
1050	58448
1550	85911
1200	87967
1600	73722
1117	54630
550	42441
1162	79596

To predict housing prices based on living area, we define a hypothesis function: h_θ(x) = θ₀ + θ₁x. Here, θ₀ and θ₁ are parameters we aim to optimize. Our goal is to minimize the error between predicted and actual prices using a cost function:

J(θ₀, θ₁) = (1/2m) Σ(h_θ(x⁽ⁱ⁾) − y⁽ⁱ⁾)²

This cost function measures the squared error over m training examples. Our objective is to find θ₀ and θ₁ that minimize this cost.

Gradient Descent

Gradient descent is an optimization algorithm used to minimize functions like our cost function. Starting with initial guesses for θ₀ and θ₁, we iteratively update them using:

θ_j := θ_j − α ∂/∂θ_j J(θ₀, θ₁)

Here, α is the learning rate. The updates continue until convergence—when changes become negligible.

Applying Gradient Descent to Linear Regression

The partial derivatives of the cost function with respect to θ₀ and θ₁ are:

∂J/∂θ₀ = (1/m) Σ(h_θ(x⁽ⁱ⁾) − y⁽ⁱ⁾)
∂J/∂θ₁ = (1/m) Σ(h_θ(x⁽ⁱ⁾) − y⁽ⁱ⁾)x⁽ⁱ⁾

Using these, we can apply gradient descent and iteratively update θ₀ and θ₁ to minimize the cost function.

Gradient Descent with Python

import numpy as np
import matplotlib.pyplot as plt

x = np.random.uniform(-4, 4, 500)
y = x + np.random.standard_normal(500) + 2.5
plt.plot(x, y, 'o')
plt.show()

def cost(X, Y, theta):
    return np.dot((np.dot(X, theta) - Y).T, (np.dot(X, theta) - Y)) / (2 * len(Y))

alpha = 0.1
theta = np.array([[0, 0]]).T
X = np.c_[np.ones(500), x]
Y = np.c_[y]
X_1 = np.c_[x].T
num_iters = 1000
cost_history = []
theta_history = []

for i in range(num_iters):
    a = np.sum(theta[0] - alpha * (1 / len(Y)) * np.sum((np.dot(X, theta) - Y)))
    b = np.sum(theta[1] - alpha * (1 / len(Y)) * np.sum(np.dot(X_1, (np.dot(X, theta) - Y))))
    theta = np.array([[a], [b]])
    cost_history.append(cost(X, Y, theta))
    theta_history.append(theta)
    if i in (1, 3, 7, 10, 14, num_iters):
        plt.plot(x, a + x * b)
        plt.title('Linear regression by gradient descent')
        plt.xlabel('x')
        plt.ylabel('y')
        plt.show()
    elif i in range(20, num_iters, 10):
        plt.plot(x, a + x * b)

print(theta)

Gradient Descent with R

x <- runif(500, -4, 4)
y <- x + rnorm(500) + 2.5

cost <- function(X, y, theta) {
  sum((X %*% theta - y)^2) / (2 * length(y))
}

alpha <- 0.1
num_iters <- 1000
cost_history <- rep(0, num_iters)
theta_history <- list(num_iters)
theta <- c(0, 0)
X <- cbind(1, x)

for (i in 1:num_iters) {
  theta[1] <- theta[1] - alpha * (1 / length(y)) * sum((X %*% theta - y))
  theta[2] <- theta[2] - alpha * (1 / length(y)) * sum((X %*% theta - y) * X[,2])
  cost_history[i] <- cost(X, y, theta)
  theta_history[[i]] <- theta
}

print(theta)

plot(x, y, col=rgb(0.2,0.4,0.6,0.4), main='Linear regression by gradient descent')
for (i in c(1,3,6,10,14,seq(20,num_iters,by=10))) {
  abline(coef=theta_history[[i]], col=rgb(0.8,0,0,0.3))
}
abline(coef=theta, col='blue')

Linear regression via gradient descent is simple and intuitive. Although advanced learning algorithms may use more complex models and cost functions, the underlying principles remain the same.

Developer Insights

What is Redis and how to begin with it

Over the last decade, dozens of open-source software applications have revolutionized technology stacks worldwide. From Linux to Mozilla, the developer community has introduced countless innovative concepts.

One such rapidly growing open-source tool is Redis — a data structure for fast in-memory data storage, used by Uber, GitHub, Twitter, Snapchat, and thousands of other companies.

But what is Redis, and how does it benefit developers?

For beginners, Redis is an in-memory data structure that can communicate with a server. If you’re looking for high-speed data storage, Redis is the go-to choice.

To simplify Redis and its importance, here’s an infographic that covers what Redis is, how to use it, and the benefits of using Redis.

What is Redis and how to use it

Read more about strings - Here

Read the basics of hash tables - Here

More about sets of strings - Here

Redis explains the Hyperlogs data structure - Here

Details on spatial indexes - Here

Benefits of using Redis module

How to install Redis

Write your first Redis module

Supported programming languages

Redis in the News

Redis at a glance

Developer Insights

Principal component analysis with linear algebra

Principal component analysis (PCA) is a powerful linear algebra-based statistical method used to reduce the dimensionality of datasets while retaining important information. It simplifies complex datasets, making them easier to analyze and visualize.

Suppose we have n individuals and measure m variables for each. Each individual’s measurements form an m-dimensional vector. For example, data collected from five individuals might look like this:

Name	A	B	C	D	E
Age	24	50	17	35	65
Height (cm)	152	175	160	170	155
IQ	108	102	95	97	87

Here, n = 5 and m = 3. Each individual's data can be written as a vector, for instance: x₁ = [24, 152, 108]ᵗ.

PCA helps answer questions such as:

Which variables are correlated?
Can we visualize this high-dimensional data more easily?
Which variables contribute most to the variation in the dataset?

Linear Transformations

Multiplying a matrix by a vector results in a linear transformation of that vector. This operation is key in PCA and is defined as: Av = w.

Eigenvectors and Eigenvalues

An eigenvector v of a matrix A satisfies Av = λv, where λ is the eigenvalue. These vectors indicate directions of data variance, remaining unchanged under transformation by A.

Spectral Theorem

For symmetric matrices, the spectral theorem ensures real eigenvalues and orthogonal eigenvectors. This property is fundamental to PCA since the covariance matrix is symmetric.

Covariance Matrix

The covariance matrix captures the variance and correlation of the dataset's variables. Its entries Sₖₗ represent the covariance between variables k and l. Diagonal entries are variances; off-diagonal entries are covariances.

Steps in PCA

Organize the dataset into an m × n matrix where each column is a sample.
Subtract the mean of each variable from the dataset (mean centering).
Compute the covariance matrix S = (1/n−1) · BBᵗ, where B is the mean-centered matrix.
Apply the spectral theorem to get eigenvalues and eigenvectors.
Select the top k eigenvectors based on the highest eigenvalues. These are the principal components.

Dimensionality Reduction

By projecting the data onto the first k principal components, we reduce the dimensions while retaining most of the dataset's variance. This simplifies analysis and visualization.

Interpreting Eigenvalues

Each eigenvalue indicates the variance captured by its corresponding eigenvector.
The sum of all eigenvalues is the total variance of the dataset.
The ratio λᵢ / (λ₁ + λ₂ + ... + λₘ) shows the proportion of variance explained by the i-th component.

Applications of PCA

Data visualization
Noise reduction
Face recognition (eigenfaces)
Genomics and bioinformatics
Market segmentation

In face recognition, PCA can reduce high-dimensional image data to a small number of significant components (eigenfaces), allowing for efficient and accurate identification based on stored components.

In the next article, we will explore gradient descent, an optimization technique commonly used in machine learning.

Developer Insights

Architecture of smart cities

Smart city services like smart water management, smart energy, smart mobility, smart buildings, etc., are based on a centralized system. A web of sensors spread across the city feeds data to this centralized system or control center by sensing activities in the physical environment. At the control center, the data is processed and stored as meaningful information. This information is shared across various departments of the city government for better coordination and data-driven decision-making.

This sounds pretty easy to implement, doesn't it?

But practically, it is not easy. Why?

Integrating various devices with different technologies with the existing communication infrastructure is one of the biggest challenges in developing a sustainable and efficient smart city.

ICT Architecture of a Smart City

The Information and Communication Technology (ICT) architecture of a smart city has four layers:

Sensing
Communication
Data
Service

Sensing Layer

This layer contains a varied set of IoT nodes deployed across an urban area. These nodes collect data about physical environment activities. An IoT node includes sensors, microchips, power supply, and network elements. Nodes are categorized as:

Constrained node: Operates in low-power environments with limited processing and data-transfer capabilities.
Unconstrained node: Has no constraints in power, processing, or data-transfer capabilities.

A node may function as constrained or unconstrained based on operational conditions. For example, a proximity sensor in a small office parking lot may be constrained, while the same in a large complex may be unconstrained.

Constrained nodes struggle with XML data due to overhead and parsing complexity. To address this, the W3C proposed the Efficient XML Interchange (EXI) format, which supports constrained devices.

EXI includes two encoding methods:

Schema-less encoding: Data is encoded directly and decoded without needing prior knowledge of the schema.
Schema-informed encoding: An XML schema is shared between processors, allowing optimized tag representation.

Schema-informed EXI enables constrained IoT nodes to become multipurpose by understanding and generating structured data formats efficiently.

Communication Layer

Every smart city system involves millions of IoT nodes. Each node requires a unique address, facilitated by IPv6 (128-bit address). However, IPv6 overhead is too high for constrained devices. Enter 6LoWPAN—a low-power protocol designed for these nodes.

Bridge routers convert IPv6 packets to 6LoWPAN and vice versa, enabling seamless communication.

Communication technologies include:

Unconstrained nodes: Wi-Fi, optic fiber, Ethernet, LTE, UMTS, and powerline communication.
Constrained nodes: IEEE 802.11 Low Power, Bluetooth Low Energy, IEEE 802.15.4, RFID, NFC, and PLC.

Data Layer

This is the intelligence layer of a smart city. It involves structured storage and processing of data from IoT nodes. Databases track:

All IoT nodes
Departments managing the nodes (e.g., water management)
Associated departmental data

Statistical models used include:

Predictive models: Analyze past and current data to forecast future events.
Descriptive models: Explain the relationship between events and their causes.
Decision models: Evaluate outcomes of decisions based on influencing factors.

ERP systems also play a role in managing interdepartmental data flows within this layer.

Service Layer

This cross-departmental layer integrates data from various city services (e.g., water, power, pollution, transport) through web and mobile applications. The layer supports not only internal government collaboration but also provides public access to subsets of data for transparency and innovation.

Many cities have addressed urban challenges successfully through smart technology. Notable examples include Barcelona, Tel Aviv, Amsterdam, Seoul, and Stockholm.

Authors

Meet our Authors

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.

Ruehie Jaiya Karri

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Hiring Strategies

7 Tech Recruiting Trends To Watch Out For In 2024

The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.

While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.

What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?

The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.

7 tech recruiting trends for 2024

6 Tech Recruiting Trends To Watch Out For In 2022

Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.

Trend #1—Leverage data-driven recruiting

Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.

Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.

The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.

A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.

Trend #3—Focus on candidate-driven market

The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.

Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.

Recruiters need to think creatively to attract and retain top talent.

Recommended read: What NOT To Do When Recruiting Fresh Talent

Trend #4—Have a diversity and inclusion oriented company culture

Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.

McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.

It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

Trend #5—Embed automation and AI into your recruitment systems

With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.

Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.

Trend #7—Be proactive in candidate engagement

Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.

As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.

Our tech recruiting platform enables you to:

Tap into a 6 million-strong developer community
Host custom hackathons to engage talent and boost your employer brand
Create online assessments to evaluate 80+ tech skills
Use dev-friendly IDEs and proctoring for reliable evaluations
Benchmark candidates against a global community
Conduct live coding interviews with FaceCode, our collaborative coding interview tool
Guide upskilling journeys via our Learning and Development platform
Integrate seamlessly with all leading ATS systems
Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide

Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Hiring Strategies

(Part 2) Essential Questions To Ask When Interviewing Developers In 2021

The first part of this blog stresses the importance of asking the right technical interview questions to assess a candidate’s coding skills. But that alone is not enough. If you want to hire the crème de la crème of the developer talent out there, you have to look for a well-rounded candidate.

Honest communication, empathy, and passion for their work are equally important as a candidate’s technical knowledge. Soft skills are like the cherry on top. They set the best of the candidates apart from the rest.

Re-examine how you are vetting your candidates. Identify the gaps in your interviews. Once you start addressing these gaps, you find developers who have the potential to be great. And those are exactly the kind of people that you want to work with!

Let’s get to it, shall we?

What constitutes a good interview question?

An ideal interview should reveal a candidate’s personality along with their technical knowledge. To formulate a comprehensive list of questions, keep in mind three important characteristics.

Questions are open-ended – questions like, “What are some of the programming languages you’re comfortable with,” instead of “Do you know this particular programming language” makes the candidate feel like they’re in control. It is also a chance to let them reply to your question in their own words.
They address the behavioral aspects of a candidate – ensure you have a few questions on your list that allow a candidate to describe a situation. A situation where a client was unhappy or a time when the developer learned a new technology. Such questions help you assess if the candidate is a good fit for the team.
There is no right or wrong answer – it is important to have a structured interview process in place. But this does not mean you have a list of standard answers in mind that you’re looking for. How candidates approach your questions shows you whether they have the makings of a successful candidate. Focus on that rather than on the actual answer itself.

Designing a conversation around these buckets of interview questions brings you to my next question, “What should you look for in each candidate to spot the best ones?”

Hire GREAT developers by asking the right questions

Before we dive deep into the interview questions, we have to think about a few things that have changed. COVID-19 has rendered working from home the new normal for the foreseeable future. As a recruiter, the onus falls upon you to understand whether the developer is comfortable working remotely and has the relevant resources to achieve maximum productivity.

#1 How do you plan your day?

Remote work gives employees the option to be flexible. You don’t have to clock in 9 hours a day as long as you get everything done on time. A developer who hasn’t always been working remotely, but has a routine in place, understands the pitfalls of working from home. It is easy to get distracted and having a schedule to fall back on ensures good productivity.

#2 Do you have experience using tools for collaboration and remote work?

Working from home reduces human interaction heavily. There is no way to just go up to your teammate’s desk and clarify issues. Virtual communication is key to getting work done. Look for what kind of remote working tools your candidate is familiar with and if they know what collaborative tools to use for different tasks.

Value-based interview questions to ask

We went around and spoke to our engineering team, and the recruiting team to see what questions they abide by; what they think makes any candidate tick.

The result? – a motley group of questions that aim to reveal the candidate’s soft skills, in addition to typical technical interview questions and test tasks.

#3 Please describe three recent projects that you worked on. What were the most interesting and challenging parts?

This is an all-encompassing question in that it lets the candidate explain at length about their work ethic—thought process, handling QA, working with a team, and managing user feedback. This also lets you dig enough to assess whether the candidate is taking credit for someone else's work or not.

#4 You’ve worked long and hard to deliver a complex feature for a client and they say it’s not what they asked for. How would you take it?

A good developer will take it in their stride, work closely with the client to find the point of disconnect, and sort out the issue. There are so many things that could go wrong or not be to the client’s liking, and it falls on the developer to remain calm and create solutions.

#5 What new programming languages or technologies have you learned recently?

While being certified in many programming languages doesn't guarantee a great developer, it still is an important technical interview question to ask. It helps highlight a thirst for knowledge and shows that the developer is eager to learn new things.

#6 What does the perfect release look like? Who is involved and what is your role?

Have the developer take you through each phase of a recent software development lifecycle. Ask them to explain their specific role in each phase in this release. This will give you an excellent perspective into a developer’s mind. Do they talk about the before and after of the release? A skilled developer would. The chances of something going wrong in a release are very high. How would the developer react? Will they be able to handle the pressure?

SUBSCRIBE to the HackerEarth blog and enrich your monthly reading with our free e-newsletter – Fresh, insightful and awesome articles straight into your inbox from around the tech recruiting world!

#7 Tell me about a time when you had to convince your lead to try a different approach?

As an example of a behavioral interview question, this is a good one. The way a developer approaches this question speaks volumes about how confident they are expressing their views, and how succinct they are in articulating those views.

#8 What have you done with all the extra hours during the pandemic?

Did you binge-watch your way through the pandemic? I’m sure every one of us has done this. Indulge in a lighthearted conversation with your candidate. This lets them talk about something they are comfortable with. Maybe they learned a new skill or took up a hobby. Get to know a candidate’s interests and little pleasures for a more rounded evaluation.

Over to you! Now that you know what aspects of a candidate to focus on, you are well-equipped to bring out the best in each candidate in their interviews. A mix of strong technical skills and interpersonal qualities is how you spot good developers for your team.

If you have more pressing interview questions to add to this list of ours, please write to us at contact@hackerearth.com.

(Part 1) Essential Questions To Ask When Recruiting Developers In 2021

The minute a developer position opens up, recruiters feel a familiar twinge of fear run down their spines. They recall their previous interview experiences, and how there seems to be a blog post a month that goes viral about bad developer interviews.

While hiring managers, especially the picky ones, would attribute this to a shortage of talented developers, what if the time has come to rethink your interview process? What if recruiters and hiring managers put too much stock into bringing out the technical aspects of each candidate and don’t put enough emphasis on their soft skills?

A report by Robert Half shows that 86% of technology leaders say it’s challenging to find IT talent. Interviewing developers should be a rewarding experience, not a challenging one. If you don’t get caught up in asking specific questions and instead design a simple conversation to gauge a candidate’s way of thinking, it throws up a lot of good insight and makes it fun too.

Developer Hiring Statistics

Asking the right technical interview questions when recruiting developers is important but so is clear communication, good work ethic, and alignment with your organization’s goals.

Let us first see what kind of technical interview questions are well-suited to revealing the coding skills and knowledge of any developer, and then tackle the behavioral aspects of the candidate that sets them apart from the rest.

Recruit GREAT developers by asking the right questions

Here are some technical interview questions that you should ask potential software engineers when interviewing.

#1 Write an algorithm for the following

Minimum Stack - Design a stack that provides 4 functions - push(item), pop, peek, and minimum, all in constant order time complexity. Then move on to coding the actual solution.
Kth Largest Element in an array - This is a standard problem with multiple solutions of best time complexity orders where N log(K) is a common one and O(N) + K log(N) is a lesser-known order. Both solutions are acceptable, not directly comparable to each other, and better than N log(N), which is sorting an array and fetching the Kth element.
Top View of a Binary Tree - Given a root node of the binary tree, return the set of all elements that will get wet if it rains on the tree. Nodes having any nodes directly above them will not get wet.
Internal implementation of a hashtable like a map/dictionary - A candidate needs to specify how key-value pairs are stored, hashing is used and collisions are handled. A good developer not only knows how to use this concept but also how it works. If the developer also knows how the data structure scales when the number of records increases in the hashtable, that is a bonus.

Algorithms demonstrate a candidate’s ability to break down a complex problem into steps. Reasoning and pattern recognition capabilities are some more factors to look for when assessing a candidate. A good candidate can code his thought process of the algorithm finalized during the discussion.

Looking for a great place to hire developers in the US? Try Jooble!

#2 Formulate solutions for the below low-level design (LLD) questions

What is LLD? In your own words, specify the different aspects covered in LLD.
Design a movie ticket booking application like BookMyShow. Ensure that your database schema is tailored for a theatre with multiple screens and takes care of booking, seat availability, seat arrangement, and seat locking. Your solution does not have to extend to the payment option.
Design a basic social media application. Design database schema and APIs for a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet.

Such questions do not have a right or wrong answer. They primarily serve to reveal a developer’s thought process and the way they approach a problem.

Recommended read: Hardest Tech Roles to Fill (+ solutions!)

#3 Some high-level design (HLD) questions

What do you understand by HLD? Can you specify the difference between LLD and HLD?
Design a social media application. In addition to designing a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet, design a timeline. After designing a timeline where you can see your followers’ tweets, scale it for a larger audience. If you still have time, try to scale it for a celebrity use case.
Design for a train ticket booking application like IRCTC. Incorporate auth, features to choose start and end stations, view available trains and available seats between two stations, save reservation of seats from start to end stations, and lock them till payment confirmation.
How will you design a basic relational database? The database should support tables, columns, basic field types like integer and text, foreign keys, and indexes. The way a developer approaches this question is important. A good developer designs a solution around storage and memory management.

Here’s a pro-tip for you. LLD questions can be answered by both beginners and experienced developers. Mostly, senior developers can be expected to answer HLD questions. Choose your interview questions set wisely, and ask questions relevant to your candidate’s experience.

#4 Have you ever worked with SQL? Write queries for a specific use case that requires multiple joins.

Example: Create a table with separate columns for student name, subject, and marks scored. Return student names and ranks of each student. The rank of a student depends on the total of marks in all subjects.

Not all developers would have experience working with SQL but some knowledge about how data is stored/structured is useful. Developers should be familiar with simple concepts like joins, retrieval queries, and the basics of DBMS.

#5 What do you think is wrong with this code?

Instead of asking developer candidates to write code on a piece of paper (which is outdated, anyway), ask them to debug existing code. This is another way to assess their technical skills. Place surreptitious errors in the code and evaluate their attention to detail.

Now that you know exactly what technical skills to look for and when questions to ask when interviewing developers, the time has come to assess the soft skills of these candidates. Part 2 of this blog throws light on the how and why of evaluating candidates based on their communication skills, work ethic, and alignment with the company’s goals.

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Hiring Strategies

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.

What is Pre-Employement Assessment?

Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.

By exploring and evaluating the best pre-employment assessment tools and tests available, you can:

Improve the accuracy and efficiency of your hiring process.
Identify top talent with the right skills and cultural fit.
Reduce the risk of bad hires.
Enhance the candidate experience by providing a clear and objective evaluation process.

This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.

Why pre-employment assessments are key in hiring

While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:

Improved decision-making:

By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.
Reduced bias:

Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.
Increased efficiency:

Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.
Enhanced candidate experience:

When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.

Types of pre-employment assessments

There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:

1. Skill Assessments:

Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.

2. Personality Assessments:

These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.

3. Cognitive Ability Tests:

These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.

4. Integrity Assessments:

These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.

By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.

Leading employment assessment tools and tests in 2024

Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:

HackerEarth:

A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.
SHL:

Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Pymetrics:

Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.
Wonderlic:

Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.
Harver:

An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.

Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.

Choosing the right pre-employment assessment tool

Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:

Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
User experience: Consider including user reviews or ratings where available.

Comparative analysis of assessment options

Instead of a comprehensive comparison, consider focusing on specific use cases:

Technical skills assessment:

Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.
Soft skills and personality assessment:

Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.
Candidate experience:

Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.

Additional tips:

Encourage readers to visit the platforms' official websites for detailed features and pricing information.
Include links to reputable third-party review sites where users share their experiences with various tools.

Best practices for using pre-employment assessment tools

Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:

Define your assessment goals:

Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?
Choose the right assessments:

Select tools that align with your defined goals and the specific requirements of the open position.
Set clear expectations:

Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.
Integrate seamlessly:

Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.
Train your team:

Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.

Interpreting assessment results accurately

Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:

Use results as one data point:

Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.
Understand score limitations:

Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.
Look for patterns and trends:

Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.
Focus on potential, not guarantees:

Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.

Choosing the right pre-employment assessment tools

Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:

Industry and role requirements:

Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.
Company culture and values:

Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.
Candidate experience:

Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.

Budget and accessibility considerations

Budget and accessibility are essential factors when choosing pre-employment assessments:

Budget:

Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.
Accessibility:

Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.

Additional Tips:

Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.

By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.

Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.

Future trends in pre-employment assessments

The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:

Artificial intelligence (AI):

AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.
Adaptive testing:

These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.
Micro-assessments:

Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.
Gamification:

Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.

Conclusion

Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.

Hiring Strategies

Tech Layoffs: What To Expect In 2024

Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?

Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.

The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.

This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.

What are tech layoffs?

The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.

In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.

Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.

This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

Yes, the market is always uncertain, but why resort to tech layoffs?

Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.

In addition, some common reasons could be:

Financial struggles

Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.

Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)

Changes in demand

The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.

Restructuring

Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.

Automation

With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.

Mergers and acquisitions

When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.

But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.

Will layoffs increase in 2024?

It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.

But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.

The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.

So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.

Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech

What types of companies are prone to tech layoffs?

Tech layoffs can occur in organizations of all sizes and various areas.

Following are some examples of companies that have experienced tech layoffs in the past:

Large tech firms

Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.

Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.

Startups

Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.

Small and medium-sized businesses

Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.

Companies in certain industries

Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.

Companies that lean on government funding

Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.

How to track tech layoffs?

You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.

Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on layoffs.

In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.

Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.

News articles

Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.

Social media

Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.

Online forums and communities

There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.

Government reports

Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.

How do companies reduce tech layoffs?

Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:

Salary reductions

Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.

Implementing a hiring freeze

Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.

Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze

Non-essential expense reduction

Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.

Reducing working hours

Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.

These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.

Tech layoffs to bleed into this year

While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.

So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)

Hiring Tools

What is Headhunting In Recruitment?: Types & How Does It Work?

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.

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Developer Insights

Top 7 most popular programming languages to learn

Top Programming Languages to Learn

Languages to Learn:

TIOBE Index

PYPL Index

Stack Overflow

GitHub

HackerEarth

Indeed

Glassdoor

Recommended Programming Languages for 2017

Why study data structures and algorithms?

What is an algorithm?

Examples, anyone?

What is Data Structure and why study it?

Now how do we connect algorithms to data structures?

Gradient descent algorithm for linear regression

Supervised Learning

Gradient Descent

Applying Gradient Descent to Linear Regression

Gradient Descent with Python

Gradient Descent with R

What is Redis and how to begin with it

Principal component analysis with linear algebra

Linear Transformations

Eigenvectors and Eigenvalues

Spectral Theorem

Covariance Matrix

Steps in PCA

Dimensionality Reduction

Interpreting Eigenvalues

Applications of PCA

Architecture of smart cities

ICT Architecture of a Smart City

Sensing Layer

Communication Layer

Data Layer

Service Layer

Technical Screening Guide: All You Need To Know

Explore HackerEarth’s top products for Hiring & Innovation

Meet our Authors

7 Tech Recruiting Trends To Watch Out For In 2024

7 tech recruiting trends for 2024

Trend #1—Leverage data-driven recruiting

Trend #2—Have impactful employer branding

Trend #3—Focus on candidate-driven market

Trend #4—Have a diversity and inclusion oriented company culture

Trend #5—Embed automation and AI into your recruitment systems

Trend #6—Conduct remote interviews

Trend #7—Be proactive in candidate engagement

Recruiting Tech Talent Just Got Easier With HackerEarth

(Part 2) Essential Questions To Ask When Interviewing Developers In 2021

What constitutes a good interview question?

Hire GREAT developers by asking the right questions

#1 How do you plan your day?

#2 Do you have experience using tools for collaboration and remote work?

Value-based interview questions to ask

#3 Please describe three recent projects that you worked on. What were the most interesting and challenging parts?

#4 You’ve worked long and hard to deliver a complex feature for a client and they say it’s not what they asked for. How would you take it?

#5 What new programming languages or technologies have you learned recently?

#6 What does the perfect release look like? Who is involved and what is your role?

#7 Tell me about a time when you had to convince your lead to try a different approach?

#8 What have you done with all the extra hours during the pandemic?

(Part 1) Essential Questions To Ask When Recruiting Developers In 2021

Recruit GREAT developers by asking the right questions

#1 Write an algorithm for the following

#2 Formulate solutions for the below low-level design (LLD) questions

#3 Some high-level design (HLD) questions

#4 Have you ever worked with SQL? Write queries for a specific use case that requires multiple joins.

#5 What do you think is wrong with this code?

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

What is Pre-Employement Assessment?

Why pre-employment assessments are key in hiring

Improved decision-making:

Reduced bias:

Increased efficiency:

Enhanced candidate experience:

Types of pre-employment assessments

1. Skill Assessments:

What is Headhunting In Recruitment?: Types & How Does It Work?