Timothy Model is a data science manager in the San Francisco Bay Area who builds measurement systems that link advertising to real business outcomes for TV networks, streaming platforms, and brands. With eight years of experience and a Ph.D. in political science from Indiana University, he applies rigorous causal methods and skeptical, research-driven thinking to design scalable incrementality products that have driven multimillion-dollar renewals and expansions. He has led teams through automation, reproducible pipelines, and methodological innovation—authoring a stratified matching uplift approach that halved compute time and introducing Bayesian attribution models that corrected biased campaign data. Beyond industry work, he co-led the CoronaNet Research Project, mentored dozens of volunteers, and published tools (including an R package) and peer-reviewed research that reflect his ability to translate complex methods into real-world impact.
8 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Political Science (Quantitative Methods), Doctor of Philosophy (Ph.D.) Political Science (Quantitative Methods) at Indiana University Bloomington
B.A. Diplomacy/Global Politics; Russian Eastern European and Eurasian Studies, B.A. Diplomacy/Global Politics; Russian Eastern European and Eurasian Studies at Miami University
Peter the Great St.Petersburg Polytechnic University
This is the data repository of the CoronaNet project on government responses to the COVID-19 pandemic and the data/code repository for the paper "A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts".
Contributions:13 pushes, 1 branch in 4 years 1 month
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