There are plenty of very good reasons to get into data science — it has the coveted one-two punch of being highly profitable and intrinsically rewarding. U.S. News ranks data scientist as number two in their list of best technology jobs, based on a number of criteria including work-life balance, growth potential, job market and levels of stress. With check marks in all of those categories and a median salary that’s nearly six figures, once a data scientist gets their foot in the door, they are set up for success.
Achieving that first step, though, is easier said than done. Acing any job interview is a challenge, especially if you don’t know what to expect. With careful preparation and research, a candidate can stack the deck in their favor — but when interviewing for their first data science job, it can be tricky to know exactly how to get ready.
Thankfully, GoHealth’s Senior Data Scientist Sanjay Sharma is here to help. Sharma sat down with Built In Chicago to share his interviewing experience and expertise, so anyone looking to land a data science role can form a strong base preparation with this stellar advice.
Tell us a little bit about your first experience interviewing for a data science role.
My first data science interview consisted of two peer panel interviews with some key team members, followed by a final executive interview. The peer panels had some general background questions followed by a few more detailed probing questions related to specific projects I had worked on and what my contributions were to each. There were also a couple open-ended problem solving questions that required some back-and-forth questioning and probing to get to the ultimate answer. The executive interview was a more back and forth discussion about me, the role and team culture, and how those all align to their strategic vision and execution of the business plan.
The hardest part of the interview for me was when I was asked to whiteboard some processing code that I had mentioned working on. Sitting down and coding something with more time and the ability to Google is very different from having to replicate it on the spot with nothing to reference. I’ve found questions like these are more often trying to measure the candidate’s ability to reason through difficult problems and articulate their thought process, rather than their ability to come up with a perfect solution on the spot.
What is the most important thing you do to prepare for a data science interview?
I always review the projects on my resume that I want to speak to during an interview. Just because you’ve spent a lot of time on a project doesn’t mean that you’ll be able to answer any question to a mixed audience off the top of your head. In particular, it’s important to be able to summarize the problem statement, the general modeling approach, the way you measured your results and how you accounted for problems along the way to someone that may be encountering these concepts for the first time.
It’s important to be able to summarize the problem statement, the general modeling approach, the way you measured your results and how you accounted for problems along the way.”
What advice do you have for someone preparing for a data science interview at your company?
“Data scientist” means something different everywhere you go, so it can be hard to know exactly what to prepare for ahead of an interview. I always suggest brushing up on some core concepts and learning about any specific techniques in the job description — or that have come up in prior interviews. Most importantly, be able to speak confidently and completely about projects you’ve worked on. At GoHealth, we’ve had more success teaching talented individuals our modeling techniques than trying to find some perfect candidate who already has a mastery of our techniques. Demonstrating that you have a deep understanding of a domain and the flexibility to pick up new ones goes a long way in a field that’s constantly evolving.
