I'm a data scientist with a PhD in molecular biology and a specialty in pharma commercial analytics. I build ML systems that help pharmaceutical companies optimize HCP targeting, measure promotional ROI, and make better resource allocation decisions.
▸ Background
My path to ML came through biology—specifically, using machine learning to discover genes essential for nitrogen fixation in cyanobacteria (work that contributed to a $7M NSF grant). That research taught me to combine domain expertise with computational methods, a pattern I've carried into industry.
I've worked at Pfizer (dynamic targeting and causal inference for commercial teams), NetApp (enterprise demand forecasting), and Lawrence Livermore National Lab (materials property prediction).
▸ What I Focus On
Pharma Commercial Analytics: HCP targeting, promotional response modeling, omnichannel resource allocation, and measuring lift with causal inference methods (propensity matching, DiD, double ML).
ML Engineering: I don't just build models—I deploy them. R Shiny, Streamlit, automated pipelines, and dashboards that let non-technical users make decisions without waiting on a data scientist.
Biology + ML: Protein fitness prediction, sequence-to-function modeling, bioinformatics pipelines. My PhD gave me intuition for biological systems that pure CS folks often lack.
▸ My Approach
I believe in ML you can explain. Stakeholders need to know why the model recommends something, not just what. I also believe in ML that actually ships—a model that can't be deployed, monitored, and maintained isn't a solution.
▸ Get in Touch
I write about ML, analytics, and complex systems at Foretodata. Feel free to connect on LinkedIn.