Analyticshala | What do Data Science companies look for in Candidates? | Prakhar Gupta

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“Never start a problem unless you are clear with the business.” These lines couldn’t be more true. It comes from Prakhar Gupta, who graduated from IIT Delhi and is now a Chief Product Officer at Syncmedia & Adtech. In addition to that, he is also a faculty team member, advisor to various data science-related work with Ivy, and has been a recruiter for many students at Ivy Professional School. This interview was focused solely on two things – the skills that data science recruiters look for in a data science aspirant and tips for a new data science aspirant for interviews.

What are the skills that Data Science recruiters look for in new aspirants?

There are various skills that one looks for in Data Science aspirants. It is not expected of anyone to have domain experience from the first day. A constant urge to learn is something that should be maintained. First and foremost is Statistics. We are introduced to statistics in our high school days, and it becomes one of the most integral parts of our data science journey. Data Science is not primarily about computer science knowledge because the applied logic comes from mathematics. Statistics help to facilitate numerous business calculations with the help of probability functions.

Secondly, it comes to Programming Language. It is necessary because one needs to communicate to the computer about the statistical analysis it will undergo. A few years ago, R was one of the most popular languages for data science, but Python has taken over recently. So no matter what the programming language is, there are chances that a new language would come up, and one might be expected to work on that. 

Third, domain experience helps only the experienced candidates. One who is good with the working procedure needs to be good with the structure of the organization. That becomes an integral part as it strengthens your presentation skills and your knowledge about the algorithms. If the domain is not clear, there will be a lack of communication, and it doesn’t help in supporting the business.

What are some of the tips for new Data Science aspirants?

The client only focuses on the output and might not focus more on the process of it. There are times where 80% of the hard work by a Data Scientist impacts 20% of the business. However, with more proactive data analysis and sharpening skills, one can achieve 80% of the work with just 20% of the hard work. Newcomers nowadays are not using state of the art. They should use a version control mechanism using tools like GitHub, GitLab, Bitbucket, etc. The focus on the concept of programming language is more important than the language itself. 

Conceptual learning will help one to adapt to any other language just by changing the syntax. Mostly, aspirants rate themselves on their programming concepts, algorithms, and statistical analysis. Any completed projects help them if they can explain them properly. For example, there are many projects available on the internet that freshers complete and are satisfied with their completion. However, the most important part is knowing the business aspect of the project and how it will help grow the business. It is essential to be able to present business insights. Being very clear about one’s role in the business chain helps in deploying the proper model. Not only the completion but also doing it properly helps to achieve the desired output. 

Asking Questions clears more doubts than any other thing:

In addition to that, it is essential to be curious and ask questions. Asking more questions helps in clearing doubts and strengthening your concepts. These concepts help in lifelong learning and not just for the interviews. It also helps explain their projects based on conceptual learning and increases their chances of cracking data science interviews. The familiarity with Project Management tools like JIRA, Confluence, etc., they are becoming a commodity and will be a default skill in the near future. Most of the data science projects come around Agile methodology.

What level of knowledge should Data Science aspirants have?

As a newcomer, it is not much expected to go far into deep learning. However, deep learning is not entirely different from machine learning, neural networks, and linear regression. Hear more about this from Prakhar himself here. As mentioned earlier, tools will keep changing, but the algorithms will remain the same. That is the most important thing to remember while working on anything – to be conceptually sound. A lot of companies also ask brain teasers in their interviews. That helps to know about the logical thinking of the interviewee, as data science revolves around logical thinking a lot. 

What is Prakhar’s favorite interview round to assess Data Science aspirants?

Prakhar explains that it depends on the level of recruiting. Instead of coding, the algorithm test is one of his favourite turns. One would prefer someone to be clear about the algorithm and have minimum knowledge about programming language than someone with the exact opposite skill. Having a positive attitude that says, “I can do it,” with proper knowledge of algorithms, recruiters look for. Learn about a beautiful instance here, where Prakhar explains his time in Malaysia. 

Watch the complete video to know more about him and learn more about preparing yourself for a data science journey. If you are such an enthusiast who wants to kickstart his/her career in data science, then Ivy Professional School has come up with many options and certifications that will help you land your dream job. So contact our wonderful team to start your bright career.

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