How This Group Product Manager Built Her Data Science Career
Western Digital is pleased to be a supporter of the Global Women in Data Science (WiDS) Podcast. This new series features women leaders across the data science profession, as they share their advice, career highlights, and lessons learned along the way.
For Sonu Durgia, Group Product Manager of Search and Discovery at one of the world’s largest ecommerce platforms, one thing has defined her career path: a willingness to change. From her career beginnings as a software engineer through positions as a business analyst, senior associate, senior finance manager, and startup founder, she’s developed a remarkable ability to take smart risks and be agile.
Now the product lead for search algorithms and computer vision at her company’s ecommerce platform, Sonu shares the story of her career journey. In the article below, we highlight four sound bites from Sonu’s recent interview. To hear the full interview, listen to the podcast interview below! (Responses have been lightly edited for clarity.)
HOST: You mentioned earlier that the current position you have is pulling all of your previous expertise together. What was your biggest challenge coming in to your current role?
SONU: I didn’t expect to be able to use everything that I had learned before applied so easily. When I came in, I knew I was going in to a brand new field and I’d have to prove myself on the technical side. What I didn’t realize, which was a pleasant surprise to me, was that a large part of my job derives from how to connect the dots. I think that’s what gives me the edge, sometimes, over others because of the variety of experiences that I’ve had; I’m able to bridge the gap and translate the language that business speaks and data scientists speak.
That was one of the biggest challenges that we had for this team, in particular, because it was such a technical team. To have the right skill set for product to be able to work with business and data scientists, really well together.
HOST: The other thing that I found really fascinating about your career is that, every step along the way, you’ve done something that’s typically male-dominated… Has it been difficult and would you have advice for females who are thinking of entering a [data science career]?
SONU: I like to think that it “shouldn’t matter”. I know it matters because as a woman doesn’t have as many role models in front of her. But, she can do whatever she wants to do if she really wants to do it. And she can be her own role model; why do we always need to look for a role model? Today, I think it’s so much easier with technology that this gap has become really small.
Back in the day, you’d just look at your peer group to find inspiration or even to solve problems or ask about a concept that you didn’t get in class… Now, everything is available through these podcasts. There’s so much information available that you don’t really have to depend on your core group around you to find those role models. And I think that will probably lead to a seismic shift in how many women can more easily do things in the next few generations. We are blessed with technology. I think that’s going to close that gender gap to a large extent. Though I’m not saying there’s not more to do; there’s more to do.
But, I think that will close the information gap that I think was one of the bigger issues for me growing up. You know, I went to an all-girls school. I was the only girl who chose engineering, at the time. I was the only girl in my engineering class, sometimes. We were only 15 in a batch of 320 students, but it was difficult. When your math professor is surprised to see you be a girl scoring 85% on a math test, when 95% of the class is at 50% or lower, it doesn’t feel good. But, at the same time, you do what you want to do, despite all that.
The approach I usually take is, you want to be the person who every man and woman wants to up their game, when you are there. And not just find a reason to think, “I am a woman, so I have to do things this way or that way.”
HOST: What have been some of the most fascinating and exciting discoveries for you in your position?
SONU: I think that one of the most interesting things that I’ve found—and this possibly goes back to the reason I guess why they hired me—was a very technical problem. We have a bunch of data scientists working with this huge amount of data around our catalogue structure and customer engagement data—hundreds of millions of queries and billions of query item interactions. It’s a lot of data, right?
You take that data and you translate that into meaningful results for a problem. But, how do you explain that to the business side and retail side, which might not be as conversant or savvy in understanding these underlying technical solutions? So, a large part of my job is being able to really understand the key problems that retail has and take that to the team to work through solutions:
- What are the most important things that we are after?
- What is our target function here?
- Are there any key features that we’re going after in our algorithms?
That has been very fascinating. What I’ve found over my career in the different digressions that you’ve seen has basically come together. My engineering degree gives me the tools to really understand the algorithms and really work with these engineers and very savvy data scientists. A background in finance gives me that, you know, bird’s eye view of understanding what the key things are: what is the key problem that we’re solving and how do we connect the dots? My Unboredly experience lets me find creative problems to solutions. You know, we have problems, sometimes, around how we tie together certain data sets, how we do certain things, and that unconventional background just gives me sometimes very creative ways to find solutions.
HOST: Looking at data science and these developments, what are you most excited about that you think will make a big difference?
SONU: I think there are lots of fascinating things happening. There’ll be some things that you’ll hear about very soon and some a little later… We recently launched this service that lets our grocery teams in the stores very quickly do quality checks on tons and tons of groceries that go through our distribution centers. That wasn’t possible before. There are regulations around food, so this service is not just for our own efficiency, it’s also to maintain our regulatory requirements. We’re also doing a lot of work on [computer] vision, voice, AR, and VR. Lots of fascinating things coming!
Find Out More Women Leaders with Data Science Careers
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