Jeremy Coyle is a Staff Data Scientist with 11 years of experience translating rigorous biostatistics and causal inference into production-ready analytics and open-source tools. A UC Berkeley PhD, he built tlverse—a targeting learning framework with 100K+ downloads—that speeds causal-methods development and has been applied to public-health work for the Gates Foundation, including interventions for childhood malnutrition. He combines statistical-methods development, experiment design, and ML to deploy estimators and monitoring algorithms at scale (e.g., remote handpump monitoring and sensor error reduction to <5%). Jeremy bridges research, engineering, and product across healthcare, international development, and energy, and has co-authored multiple papers in Nature while also placing third in a national Long COVID prediction competition. He’s equally comfortable shipping KPI dashboards and open-source libraries as he is defining study scope and translating technical results into actionable policy insights.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Biostatistics, Doctor of Philosophy - PhD, Biostatistics at University of California, Berkeley
Contributions:1 PR, 46 pushes, 3 branches in 2 years 5 months
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