Meet Siobhan McDermott, MS in Applied Data Science Alumna

Siobhan McDermott (MS in Applied Data Science ’24) is a Clinical Research Professional at the University of Cincinnati’s Department of Radiology, where she works on clinical trials and stroke research with a focus on biomedical imaging. With over a decade of experience in healthcare research, Siobhan uses data science to improve how clinical data is collected, managed, and applied in practice.

We spoke with Siobhan about her path into data science, her experience in the MS-ADS program, and what excites her most about the future of healthcare research.

Before joining the MS in Applied Data Science program, what was your academic and professional background? Was this always a field you were interested in?

Yes. It really began when I was getting my bachelor’s degree at Ohio State University in psychology. I had the opportunity to participate in clinical research, and that was kind of an aha moment for me. I became really drawn to neuroscience research and biomedical imaging. I loved the rigor of scientific inquiry, and it was something I was always interested in.

I worked on everything from very small, personalized studies to large, national and global pharmaceutical trials, particularly focused on helping people with neurological disorders like Alzheimer’s disease and multiple sclerosis. I’ve always liked asking questions, understanding hypotheses, and finding truth in the world. That curiosity has really guided everything I’ve done for probably the last decade.

What led you to choose the University of Chicago’s MS-ADS program?

I had been working in clinical roles for a few years, and while it was really fulfilling, I became more interested in the back end, how research questions are posed and how inquiry develops as a process.

At the same time, there was this proliferation of new tools, especially artificial intelligence, becoming part of the cultural awareness. I was really interested in how those tools could be used to improve healthcare, and that’s what led me to data science.

UChicago’s program stood out to me because the course offerings were so varied. I was looking for a more holistic education, a kind of one-stop shop for learning how to actually create these tools. It’s a very reputable program at a well-known institution, and I felt like it would give me an intensive understanding of what’s needed to navigate all these changes happening in the field.

Is there something you’re particularly proud of accomplishing during the MS-ADS program?

I really loved my capstone project. All of the courses in the program were very collaborative and team-focused, but the capstone stood out to me.

What I really wanted was a practical understanding of how these tools move from ideas into industry. The two-semester structure of the capstone allowed me to really dive into how things operate behind the scenes, how decisions get made and how tools are implemented in real contexts. That experience was incredibly fulfilling.

How did the MS-ADS program help you bridge clinical research and data science?

It was completely transformational for me. When I started the program, I had a strong healthcare focus, but I wasn’t as well-versed in how data science tools worked or how they were adopted at an industry level.

By the end of the program, I had the fundamentals I needed, from data mining to understanding the different software tools available today. Having instructors who have actually used these tools walk you through how they work and how they’re applied is an education you really can’t just pick up anywhere. I’m very grateful for that experience.

How are you using data science in your current role at the University of Cincinnati?

I’m working in clinical trials and stroke research, with a focus on biomedical imaging. Part of my role involves managing the imaging data created during trials and overseeing how it’s collected.

Understanding good data principles and how the back end works has been really foundational for me. It helps me anticipate problems before they happen and think about how we can streamline processes. Healthcare teams are extremely strapped for time, so understanding the downstream consequences of data decisions has been really important in helping guide where we want to go in the future.

What advice would you give to someone interested in pursuing a similar path?

I would say: stay curious. Data science is a huge field, and everyone is doing something different. I tried to take a wide range of courses in the program so I could understand different concepts and tools, even ones I didn’t think I’d use day-to-day.

That breadth really mattered for me, because it exposed me to areas I didn’t even realize I was interested in. Even if you don’t end up using every skill directly, understanding the landscape is incredibly valuable.

What are you most excited about exploring next in your work or research?

I’m very excited about the future of biomedical imaging in stroke research, things like MRI and CT scans. There’s already been a lot of work to improve diagnosis and decision-making for physicians using these tools, and I think we’re on the horizon of seeing them integrated more fully into day-to-day clinical practice.

I’m very optimistic and genuinely excited to see how these tools continue to develop and become part of everyday healthcare.

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If you’re interested in reading more stories from the MS in Applied Data Science community, read how Hank Snowdon applied data science to a career in Major League Baseball, or how Thien-An Bui turned a mock interview into a real job opportunity. You can also check out our upcoming information sessions and events here.