A Day in the Life of a Data Scientist: Shantanu Kumar

. 3 min read

Perhaps you are considering a career switch to Data Science. Maybe, you've just finished a program, and you want to know what comes next. Or, you're curious about what the mysterious figures in your office are doing, and why everyone thinks they are so important.

GreyAtom spoke to Data Scientists in the industry, asking them to detail a typical working day in their lives. We've collected some insightful responses, shedding light on a job that is still not completely understood by many people.

Shantanu Kumar - Head of AI Products, inFeedo

What are the business problems you solve with the help of data science on a day-to-day basis?

Some business problems we're solving using data science:

  • Actionable insights for CHRO's using textual analytics
  • How can we predict attrition in various organisations?
  • How can we find areas of disengagement within an organisation?

What does your typical workday look like? How do you distribute your time across different activities?

I lead the end-to-end development of all AI-related features that are embedded into inFeedo’s products. We've recently developed a state of the art NLP engine in the AI/HR space that boasts of domain-specific textual analytics, which is powered by a proprietary algorithm called LexScore. LexScore helps drive actionable and effective action within the organisation. This involves a huge chunk of my bandwidth in terms of:

  • Gathering market intelligence & research and development of new features
  • Ideating with my team to develop GTM strategies
  • Data analytics and engineering to find metrics that back our hypothesis
  • Developing an extensive prototype to test out with customers
  • Deploying and tracking success metrics

Which teams and stakeholders do you work with?

Since product ideation is an integral part of our work, working with the product and design teams along with tech teams for integrations is something we do.

How do you build your own capabilities and those of your team? How do you learn newer technologies?

We're primarily learning by iterations – the more the merrier! Picking up use-cases to solve for and exploring multiples solutions around it, as we go through each one of them, helps us learn a lot!

On the side, we contribute to open-source projects and pick up stuff from there. Plus, following educational and futuristic opinion blogs and tech leaders.

What are the most rewarding/ frustrating moments in your journey as a data scientist?

One of the most rewarding moments in Data Science for me remains how we can add immense value to businesses by crunching large volumes of data – drawing insights that were not visible before.

What still does remain frustrating is the number of solutions we need to explore and check the viability of, before we call it something that truly does "work".

What according to you are typical behavioural and technical traits needed in a data scientist?

The primary behavioural trait I've noticed in successful data scientists is perseverance and the commitment to keep exploring and moving forward. Since this role involves trashing a ton of your own work before you are satisfied with the results, it does need effort to throw away things personal to you and that have involved lots of work, while you start over.

Technically, data scientists should be good with data. Period. Regardless of what tool they're using - be it Excel, R, Python or whatever. Knowing how to present data and why they're selecting a particular sample over the other is very important. Being good with data essentially means having strong domain knowledge of what data is important and what isn't, since most code is openly available these days.

Interested in being featured is our series? Write a comment below, or drop us a line, and we'll be in touch!

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