Meet Our Mentors: ML Engineer Chirag Mahapatra
Between his first internship at Goldman Sachs and his current work at Airbnb, Chirag Mahapatra has been guided by mentors who challenged him and pushed him to improve. As a mentor to Data Science Career Track students, he hopes to provide the same motivation.
We recently sat down with Chirag to discuss the toughest and most rewarding parts about his work, and what he thinks it takes to excel in a data science role.
How did you get started in data science?
I started working in data science way back in my first internship at Goldman Sachs. At that point in time, the position wasn’t called data science—at least no one called it that then. It was more of an analyst role. I was building models to understand whether certain financial decisions are good or bad. And that’s where I picked some of the skills up.
I kind of learned about the term data science during my graduate studies, where I was studying operations research in statistics. That’s kind of where I felt like, OK, this is actually a cool profession and so it makes sense to try it out and look for jobs.
I was fortunate to get a job on Amazon’s data platform team as my first job after graduate studies and I think that’s where I learnt a lot about what exactly is data science, how’s data used in big companies, how does data engineering work. I think all those experiences kind of gave me an insight into the field and helped me.
What’s the toughest part about being a data scientist?
Communicating the value of your work to other stakeholders, especially when you’re doing experiments which take a long period of time. You’re actually investing a large part of the company’s resources to that experiment, so you need to be clear about what you’re expecting to see at the end of this work and then have a nice story to tell around that work so that you can communicate to the leadership team or even your peers, like, why is this taking so much time and what exactly are we doing, what are we hoping to gain from it.
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What’s been the most rewarding part of being a data scientist?
I’ve been fortunate to have worked on a couple of experiments which have shown positive results and it’s been really satisfying to have a hypothesis proved. And of course, it’s always really rewarding when you actually drive value for the company based on what you do.
What does it take to excel in a data science role?
To excel in a data science role, you probably need to be really curious and to have that kind of mind where you’re constantly learning. This field is changing so rapidly that no qualification or degree can actually prepare you for it in the long run, so I think the onus is on each data scientist themselves to keep on learning, keep on building themselves, understand different places where their skills can be applied and then apply them.
What impresses you when hiring?
I remember this one candidate who had, for his interview, brought his portfolio of work. And the great thing about this candidate is he brought his portfolio, he had these websites which showed his work, he had these amazing visualizations, he was able to communicate all of them well. Many candidates would talk about the results of their work, but they actually wouldn’t show it. He brought demos to the interview and that really helped us understand his projects and work better.
Why did you decide to be a Springboard mentor?
I found Springboard to be an amazing experiment and it’s basically changing this notion of how education is supposed to work. I think that’s really fascinating and I wanted to be a part of it. I think being a mentor gives you an opportunity to positively impact a few students’ lives.
How has a mentor helped you in your career?
I’m really fortunate to have many amazing mentors in my career and I think a good mentor challenges you. They do not tell you what you want to hear, but they actually challenge you to improve yourself, to identify ways you can deliver more impact. Many of my mentors, when I thought I was doing really good work, they were like, OK, hey, maybe you are, but how can you do this better? How can you push yourself even further?
How do you mentor others?
I really try to be honest in my conversations about where I see the candidate going and I try to understand what the candidate really wants and then see if those two align themselves. Many times, students have a preconceived notion of what data science is or what kind of job they want and sometimes it may not be true, so I try to help them out, saying, “Hey, maybe this is not the best fit for you, maybe this is a better fit for you, and this is what you should be doing. And this is based on the skills I have seen, so if you have this other set of skills maybe you should work harder to show them.”
This Q&A has been lightly edited and condensed for clarity.
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