Summary
Jamie Carter is a Senior Data Scientist at the Urban Institute who builds and applies microsimulation and ML methods to evaluate health insurance policy changes. With an MS in Public Policy & Data Analytics from Carnegie Mellon and four years of applied research experience, Jamie bridges rigorous policy analysis and production-ready data tools. Past roles include poverty research at the Congressional Research Service and a Data Science Fellowship at the IRS where they produced an algorithmic bias toolkit and evaluated fairness libraries like AIF360. They’ve led cross-functional teams to deliver civic-facing products (e.g., AssetMappr) and built interactive Python/Dash apps that integrate census and education data for community decision-making. Based in Pittsburgh, Jamie combines technical depth in Python and ML with a practical focus on auditable, policy-relevant outcomes. An attention to operationalizing fairness—rather than just measuring it—distinguishes their approach.
4 years of coding experience
5 years of employment as a software developer
High School, High School at The Blake School
BA, Economics, BA, Economics at Carleton College
Postgraduate, Postgraduate at The Hill School
MS, Public Policy & Data Analytics, MS, Public Policy & Data Analytics at Carnegie Mellon University - Heinz College of Information Systems and Public Policy
burmese (myanmar), English