Summary
Daniel Forsyth is a Senior Data Scientist with 12 years of experience applying machine learning and data engineering to healthcare challenges, currently driving predictive initiatives at Hackensack Meridian Health. He has deep hands-on expertise building end-to-end analytics products—from SQL and Airflow pipelines to Shiny/Flask dashboards and deployed models on cloud platforms—and a track record of translating EHR, registry, and genomics data into operational tools for clinical teams. Previously he led clinical research analytics at Sidney Kimmel Cancer Center, where he created protocol QC, accrual gap monitoring, and enterprise feasibility workflows while establishing reproducible deployment practices and mentoring analysts. His research background includes published ML work on wearable data for stress detection and early neonatal sepsis prediction, reflecting a blend of academic rigor and production impact. Known for standardizing toolchains (Posit Connect, R Markdown) and championing data literacy, he combines applied ML, feature engineering, and pragmatic productization to improve patient- and trial-focused decision making.
12 years of coding experience
9 years of employment as a software developer
Bachelor of Science (BS) Information Systems, Bachelor of Science (BS) Information Systems at Drexel University
Masters Coursework Computer Science Focus on Machine Learning, Masters Coursework Computer Science Focus on Machine Learning at Georgia Institute of Technology