The demand for data scientists—a role Harvard Business Review once famously dubbed “the sexiest job of the 21st century”—has never been higher. Today, seemingly every company, government, nonprofit, and university needs talented data scientists like Megan Hawley ‘23 to turn raw data into actionable insights.
Hawley’s story illustrates the versatility of a data science degree and its usefulness across sectors, or what she described as “cross-industry appeal.” Over the past three years, she has leveraged her skills to track tuna populations for Ocean Conservancy, to help match students with potential dating partners through Datamatch, and most recently, to develop machine learning models for the Minnesota-based automotive company, Polaris Inc.
We asked Hawley about her time at UW-Madison, her current work with Polaris, her thoughts on why data science is such a fast-growing field, and her tips for current students. The conversation has been condensed for brevity and clarity.
How did you end up studying Statistics and Data Science at UW-Madison?
I grew up in the Milwaukee area and only applied to UW-Madison and Minnesota, but after visiting Madison, I knew right away that this is where I wanted to be. For my major, I had always loved math and technology, and I was initially leaning toward Computer Sciences. But then I found Data Science, which merged the data aspect with the technology side, and that seemed like a perfect fit.
As a sophomore, I decided to also declare as a Statistics major, which I think is a great pairing because Statistics helps you get a deeper understanding of the mathematical foundations of data science.
Talk a bit more about your experience in the Department of Statistics. What experiences or courses stand out to you and why?
One of my all-time favorite classes was STAT 405: Data Science Computing Project. I learned a ton about big data systems, and I feel like it really prepared me more for a professional role, which I appreciated. It was taught by John Gillett, who always made sure we understood how the class material could be applied in the real world.
I also helped Brian Powers, another Statistics faculty member, on a research project with Ocean Conservancy. We were working on a model called POSEIDON, designed to use big data and computing to improve fisheries management and make it more sustainable. It was a cool experience, especially because Ocean Conservancy is clearly doing a lot of good in the world.
Finally, I had great experiences in several student organizations, especially Datamatch. It’s a student club that creates a survey every Valentine’s Day and matches people on campus for dates. I met many of my closest friends in that club.
After graduation, you started a new job at Polaris Inc. What does this role involve?
After I met with Polaris at a UW-Madison career fair, they interviewed me and offered me the chance to participate in a two-year program that gives me the chance to explore four different roles over the course of two years. The first rotation, which I’m in now, is machine learning engineer, which I’m excited about. I help ensure all of Polaris’ machine learning models are operating effectively, and I also help create new models to aid in business decision-making.
A few other UW-Madison alumni have done this program, and it’s fantastic because committing to one role right out of college is difficult. Most recent graduates don’t have a ton of experience to know what roles they thrive in, so this type of program gives us the chance to try out multiple roles.
What are your thoughts on the rapid growth in the field of data science more broadly?
Some companies have understood the power of data science for a while, but others are just realizing how important it can be. As a result, tons of them are either expanding their data science departments or creating them from scratch, and basically every company in every industry is looking for data scientists.
But I’d also add that the field of data science is changing almost as fast as it’s growing. Ten years from now, it will be very different, partially because technologies like generative AI are developing so fast and impacting how data scientists do their jobs. Still, with a good education in the foundations of data science, like the one I got at UW-Madison, I feel well equipped to keep up with changes in the field.
Do you have any guidance for current students hoping to pursue data science careers?
Internships are incredibly helpful for a future job search in data science, and they help you get a first glimpse at the professional world you’ll be entering after graduation. I definitely recommend current students do all they can to pursue internships, including by utilizing SuccessWorks. They helped me find an internship and craft my resume, and more students should take advantage of their resources.
There is also s CDIS career fair every semester, which gives students a great chance to meet companies one-on-one, to network and explore internship and potential career opportunities before they graduate. I’d definitely recommend data science students go to as many of those as possible.
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