4 Women Leaders in Health Data Science Share Career Advice

Happy Women's History Month!

Discover valuable career advice from four top women leaders in health data science. Learn from their experiences and insights to advance your career in this field.

Susanna Supalla, PhD

https://www.linkedin.com/in/susanna-supalla/

Susanna Supalla, PhD is a Staff Data Scientist at Hinge Health, a digital clinic for joint and muscle care and virtual physical therapy. She is the owner, Sparkline Insights LLC, a one-member data consultancy. Currently, she resides in Washington, DC, working remotely.

I’m often asked how one trains to become a data scientist. My answer is always the same: practice learning deeply. There are many ways to pick up the technical skills required in data science, but in the end, you often need a methodology you haven’t yet learned. So the hardest part is quickly picking up technical knowledge AND the subject matter expertise required to understand the context of the data. So in college or grad school, study data science, statistics, or computer science AND a substantive subject. Do repeated projects within the same domain. Become an expert in something -- anything --, and that will teach you how to learn about any area you may be called upon to analyze.

Jana Gunn, MS

https://www.linkedin.com/in/jana-gunn/

My name is Jana Gunn, and I am the Sr Director of Advanced Analytics at Blue Cross Blue Shield of Minnesota. I've had the opportunity to work in various areas within healthcare analytics throughout my career, including Reporting and Analysis, Underwriting, Healthcare Economics, Consumer Analytics, and Data Science. I earned my Bachelor's degree in Business Administration from the University of Illinois Urbana-Champaign and a Masters's degree in Predictive Analytics (Data Science) from Northwestern University.

There are three areas of advice that I would give to a health data scientist just entering the field. The first is never to stop learning. The field is constantly changing, so you must have the mindset that there is always something new to learn. Whether it be learning about emerging trends, new technologies, or just other areas of the business, a successful data scientist should be on the lookout for opportunities to learn new things. The second piece of advice is to network as much as possible. Attend conferences, join hackathons, write a blog, and find a mentor. Find opportunities to meet new people and make a name for yourself. While it may not seem like it, the healthcare industry is small, and you never know when your network might come in handy. My third piece of advice is to develop strong communication skills. Being able to describe technical concepts to a non-technical audience is key to being a strong data scientist. This skill will help ensure that the business understands the value of your work and can determine whether or not your project gets implemented or sits on the shelf. Be willing to present your work, teach others, and speak up at meetings. The more you communicate, the better you get at it.

Hiywete Solomon, MPH

https://www.linkedin.com/in/hiywete/

I’m Hiywete Solomon, and I am an AD of Business Process at Optum Health. I have my master’s degree in Biostatistics from Yale School of Public Health and my bachelor’s in economics from Wellesley College. For the past 10+ years, I have worked in various healthcare data and analytics roles as an individual contributor and a team lead.

Healthcare is large, and many areas within it often need data analytics/data science support. I advise up-and-coming health data scientists to find something that excites you about your work and build trust with stakeholders.

1) Explore the things you find rewarding. Over the course of my career, my work has been most rewarding when I focus on a subject and understand how features/drivers of a model or metric are potentially influenced by a change to policy or regulation I am already tracking. This has allowed me to build subject matter expertise. Find something you consider rewarding in the field and continue honing your skills.

2) Build Trust. When I started working in analytics, I was surprised to see predictive models sitting on a shelf because stakeholders at the time struggled to trust and understand the potential benefits of leveraging these models. Fast forward, we’re now in an era where there’s a lot of interest in machine learning and many analysts trained at building models and analytic tools. However, trust is sometimes still in question. Therefore, it’s important to build trust with your teammates and clients. One of the most effective ways to do this is to include them throughout the various steps in your project’s life cycle; this helps build rapport with them and improves their confidence in data science.

Tracy Romano, PhD

https://www.linkedin.com/in/tracyannromanophd/

My name is Tracy Romano, and I work as a Senior Data Scientist at Blue Cross North Carolina. My academic background is rooted in the fields of psychology and computational neuroscience. In addition, I have garnered diverse industry experience throughout my professional career, having worked in the federal government, cybersecurity, the airline industry, and health care. This varied professional background provides me with a unique blend of technical skills that enhance the quality of my work in the health care sector.

As someone who has been doing analysis for a significant period, I can offer three valuable pieces of advice to those entering the health data science field. (1) Data Science is an academic field that encompasses endless information. It is neither necessary nor possible to master every facet of the discipline or have firsthand experience in every area. While experience is undoubtedly valuable, it is equally crucial to identify, comprehend, and apply new methods accurately. (2) To avoid limiting your potential, focus on developing proficiency in programming languages like Python, with particular emphasis on computational thinking. (3) Performing a literature review is a crucial initial step in building a model or metric. Understanding your content guides your building process, aids decision-making, and promotes a deeper understanding of your data. Comparing your results with existing literature confirms your findings and may lead to discoveries.

Do you have advice to share? Please leave a comment below.