Dylan Pieper

Data Scientist

My academic background is in social science, which developed into a career in data science and data and analytics engineering. I lead all efforts related to data in research, program evaluation, and business operations. I thrive when working toward a human-centered mission and collaborate with teams to create principled, insightful, and beautiful data tools. I work across the full stack—from the visualizations, reports, and dashboards people interact with to the models, pipelines, and cloud architecture that scale them—grounded in reproducibility and open science.

I currently lead program evaluation and data analytics at UW-Madison, advancing medical education. Across my career, I’ve transformed big and messy datasets—from APIs, surveys, and health records—into actionable insights presented in reports, dashboards, and web applications to stakeholders. My work has reached audiences worldwide through open-source packages, talks, and publications.

My technical foundation is built on R, Python, and SQL, allowing me to seamlessly move from data extraction to advanced modeling. I leverage statistical inference, ML, and AI to turn complex datasets into predictive insights. To deliver these insights to stakeholders, I build and publish interactive data products using tools like Quarto and Shiny, maintaining deployment workflows through GitHub and GitLab CI/CD pipelines and containers on platforms like Posit Connect and Azure.

Outside of data science, I have a deep appreciation for nature and spirituality. From paragliding to yoga, I find energy and balance through exploring outer and inner landscapes.