Dr. Priya Sharma
Chief Data Scientist
HealthTech Analytics
About
Dr. Priya Sharma leads a team applying machine learning to improve healthcare outcomes. Her work has helped hospitals predict patient readmission rates and optimize resource allocation. She holds a PhD in Statistics from UC Berkeley and is a vocal advocate for ethical AI development and diverse representation in data science teams.
“Data science is one of the most impactful fields you can enter today. Your unique perspective as a young woman will help you ask questions that others miss — and those questions lead to breakthroughs.”
Interview
What is data science, and why does it matter?
Data science is about finding patterns and insights in data to solve real-world problems. It combines statistics, computer science, and domain knowledge. It matters because data drives decisions in every industry — from healthcare to education to climate science. A good data scientist can take messy, complex information and turn it into something actionable that helps people.
What path did you take to become a data scientist?
I actually started as a math major in college. I loved statistics and probability but wasn't sure how to apply them practically. Then I took a course in computational biology and realized that math could be used to solve health problems. I went on to get a PhD in Statistics, focusing on biostatistics, and then transitioned into industry where I could apply those skills at scale.
How do you think about ethics in AI and data science?
Ethics should be woven into every stage of a data science project, not treated as an afterthought. We have to ask: whose data are we using? Could our model be biased? Who benefits and who might be harmed? Having diverse teams helps catch blind spots. I've seen firsthand how a homogeneous team can build a model that works well for some populations but fails for others. Diversity isn't just a nice-to-have — it's essential for building fair technology.
What can students do now to prepare for a career in data science?
Learn Python — it's the most widely used language in data science and there are tons of free tutorials. Practice with real datasets on Kaggle. Take statistics courses seriously; they're the foundation. But also, develop your communication skills. The best data scientists can tell a story with data and explain their findings to non-technical stakeholders. That combination of technical and communication skills is rare and incredibly valuable.