Diving Deep Into The World Of Data Science With Ashutosh Kumar

Hire IQ by HackerEarth is a new initiative in which we speak with recruiters, talent acquisition managers, and hiring managers from across the globe, and ask them pertinent questions on the issues that ail the tech recruiting world.

Next up in this edition is Ashutosh Kumar, Director of Data Science, at Epsilon India.

We had a long chat about hiring for niche roles like data science and data analysts, whether there will still be a need for such roles post this layoff phase, and expert tips that developers can make use of to excel in these roles.

Dive in!

P.S. If you missed the previous edition of HireIQ where we sat down with Patricia Gatlin, Diversity Lead/Talent Sourcing Specialist, at Johns Hopkins, you can read it here 🙂

Let’s delve into the future of data science

HackerEarth: Can you give us a small bio of your journey in tech recruitment?

Ashutosh: I have been a part of recruitment in the data science field for nearly 14 years of my career and have recruited for successful startups (seed to Series D) and MNCs across levels (entry, junior, mid and senior management) and profiles including data analysts, data scientist, ML engineers, full stack developers, and DevOps/MLOps. I’ve also been part of campus recruitments in premier colleges (IITs, NITs, IIMs, and ISB) for roles in data science profiles, as well as the lateral hiring processes for experienced candidates for almost all my previous employers.

HackerEarth: In this era of mass layoffs, where do you see the data science and data analyst roles heading? Will there still be a need for this niche domain going forward?

Ashutosh: Mass layoffs depend on the health of a company and its measures to keep itself up and running and have less to do with any specific roles. Companies can cut all types of roles when it comes to survivability, but domains like data science and technology are some of the last ones to be axed since these are business-critical roles.

Let's delve into the future of data science

For instance, several of our clients, who are facing the pressures of recession, have been turning to data science to gather data-based insights on how to increase their revenue and save costs. Data science plays an important role in helping companies navigate and weather the recession storm.

We are a data-driven world, and data science will continue to be an in-demand domain. The demand for data science and data analysis professionals may fluctuate depending on economic conditions and the specific needs of individual organizations. It is important for professionals in these fields to stay up to date with the latest technologies and techniques, and to be proactive in seeking out new opportunities for growth and development.

Also read: Inside The Mind Of A Data Scientist

HackerEarth: What are some of the mistakes/misconceptions (top 3) that you have seen recruiters or engineering managers make when hiring data scientists/data analysts?

Ashutosh: Firstly, focusing only on interviews and theoretical questions instead of looking for hands-on coding experience is a big mistake. The industry needs people who can not only understand algorithms but who can also code. It’s fairly easy to get a theoretical understanding of all data science algorithms from the internet without writing a single line of code, and we need to ensure we hire people who can actually build solutions.

Secondly, giving importance to degrees and background over expertise. Today, there’s a plethora of online degrees which require little effort for a diploma or master’s degree in data science – one can get a degree from Indian or international colleges for ~USD 4000. Some of the best data science professionals we’ve worked with have unrelated degrees and have learned everything by themselves – either from online courses, Kaggle, blogs, or self-training.

Lastly, every data-related skill cannot be equated with data science and AI. The latter’s expanse is wide and complex – from simpler tasks like data entry, to intermediate ones like analysis, visualization, and insights, and to the more advanced machine learning models and AI algorithms. Often, roles are clubbed as ‘data scientist’ simply because of such loose definitions of these terms. You don’t need to hire a data scientist when you may actually need a data analyst.

HackerEarth: How do you see the new technologies like AI, ML, and quantum computing affect the field of data science?

Ashutosh: AI, machine learning, and quantum computing are all rapidly advancing technologies that have a significant impact on data science. AI and machine learning are enabling data scientists to develop more advanced algorithms and models that can analyze and interpret data more effectively, while quantum computing is providing the computing power necessary to process and analyze large amounts of data quickly and accurately. These technologies are also helping automate many of the tasks that were previously done manually, which is making data analysis more efficient and accessible. Overall, these new technologies are helping drive significant advances in the field of data science and are likely to continue to do so in the future.

Also, read: How AI Is Transforming The Talent Acquisition Process In Tech

HackerEarth: How would you recommend that data scientists upskill themselves to cope with the current and upcoming changes in the economy and the tech world?

Ashutosh: As a data scientist, it is important to continually upskill and stay current with the latest developments in the field. Here are a few ways data scientists can upskill themselves:

Stay updated on the latest tools and technologies: Data science is a rapidly evolving field, and new tools and technologies are constantly being developed. There are new algorithms in the domain of Deep Learning, Reinforcement Learning, Transfer Learning, LightGBM, GANs, Transformers, large language models, and Explainable AI to name a few. There are new tools and frameworks in the industry like Airflow, Horovod, Petastorm, etc. developed by companies like Facebook and Uber, which have been made open source. There are also AutoML, ETL tools, visualization tools, cloud enablement tools, collaboration, and project management tools (like Asana and Trello). Keep abreast of these advancements and use them effectively in your work.
Learn new programming languages and frameworks: As a data scientist, you’ll need to be proficient in at least one programming language, such as Python or R. It is also useful to learn additional languages and frameworks such as SQL, Julia, or TensorFlow.
Enhance your machine learning skills: Machine learning is a key aspect of data science, and it’s important to have a strong foundation in this area. There are many online courses and resources available to help you learn machine learning and apply it to real-world problems.
Stay informed about industry trends and developments: There are various forums on the internet that track the latest trends and developments in data science and machine learning. I follow researchers, data scientists, machine learning experts, and AI/ML companies on Twitter which is a great source of the latest information in this field. There are also freely available YouTube videos and podcasts one could make use of. There are many discord channels for every area – algorithms, MLOPs, software engineering, deployments, etc. and you can join the ones related to your area of interest and expertise. This will help you identify new opportunities and stay ahead of the curve.
Network and collaborate with other professionals: You can join meetups in your city or area to connect with other professionals in this field to know about the developments and research being done elsewhere. There are a lot of ML conferences and hackathons that happen throughout the year which are a great source of learning as well as networking with other professionals. LinkedIn groups and forums, industry events, and community workshops are also great ways to learn from others and stay up to date with the latest trends in the field.

HackerEarth: Your final word to developers in this stream: What do you developers need to know to excel in data analytics or data security and what are your top 3 expert tips?

Ashutosh: To excel in data analytics, developers should have a strong foundation in math and statistics, as well as programming skills. They should be proficient in using tools and technologies for data manipulation, visualization, and analysis, such as SQL, Python, and R. In addition, they should have strong communication and problem-solving skills, as they will often be working with large and complex datasets and will have to clearly present their findings and recommendations to stakeholders.

Here are my top 3 tips for developers interested in pursuing a career in data analytics:

Practice, practice, practice: The best way to improve your skills in data analytics is to get hands-on experience working with real data. This can involve working on personal projects, participating in online hackathons or data science competitions, or taking on internships or freelance projects.
Stay up to date: The field of data analytics is constantly evolving. Follow the latest technologies and best practices in order to remain competitive in the job market. This can involve reading industry blogs and news, attending conferences and workshops, and taking online courses to learn new skills.
Build a strong network: Networking is an important aspect of any career and is especially important in the field of data analytics. Building relationships with other professionals in the field can help you stay connected to the latest trends and opportunities and can also provide valuable mentorship and guidance as you progress in your career.

HackerEarth: Your final word to recruiters hiring for the role: What specialized tools do you think they should be using, what markers of skill should they be looking for, and how can they improve their own understanding of the domain in order to hire better?

Ashutosh: As a recruiter or hiring manager for data science roles, it can be helpful to use specialized tools and platforms to identify and evaluate candidates. Some options may include:

Online coding platforms: These allow candidates to complete coding challenges or take technical assessments to demonstrate their skills. Examples include HackerEarth, CodeSignal, and TopCoder.
Data science-specific job boards: There are several job boards specifically focused on data science roles, such as Kaggle Jobs and Data Science Central. These can be good places to find candidates with relevant experience and skills.
Resume screening software: Tools like Lever and Jobvite can help automate the resume review process by identifying keywords and qualifications relevant to the role.

Also, read: 10 Tech Recruiting Strategies To Find The Best Tech Talent

In terms of markers of skills, there are a few key areas to focus on when evaluating candidates for data science roles:

Technical skills: Look for candidates with strong programming skills, as well as experience with data manipulation, visualization, and analysis tools such as SQL, Excel, and data analysis libraries like Pandas and NumPy. Experience with machine learning libraries like sci-kit-learn, TensorFlow, and Keras can also be valuable.
Problem-solving skills: Data scientists should be able to identify and define problems, develop hypotheses and models, and evaluate the results of their work. Look for candidates who have a track record of successfully tackling data-driven projects and can demonstrate the results they achieved.
Communication and collaboration skills: Data scientists should be able to clearly articulate their methods and findings to both technical and non-technical audiences, and work effectively as part of a team. Look for candidates who have strong verbal and written communication skills, as well as the ability to work well with others.
Domain expertise: It can be helpful to look for candidates who have a strong understanding of the specific domain or industry in which they will be working. This can help ensure that they are able to apply their skills and knowledge in a way that is relevant and impactful.

To improve their own understanding of the domain, recruiters can seek out training and education opportunities, such as online courses or industry conferences. They can also stay up to date on the latest developments and best practices in data science by reading articles and publications in the field.

About Ashutosh Kumar:

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Ashutosh Kumar is working as a Director, Data Science at Epsilon focusing on Marketing Machine Learning as a part of the Strategy and Insights (S&I) group. He is involved in building Data Science products with a team of data scientists, data and ML engineers, and full-stack developers. At Epsilon, he is also building the Marketing Machine Learning team with freshers and lateral hires, and upskilling them with the latest tools and technologies.

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Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

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Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin — This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

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A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.