Summary
Conor Healy is a data science leader and educator with eight years of focused experience designing experimentation, causal inference, and platform-level measurement for consumer ecosystems serving hundreds of millions of users. He builds scalable analytics frameworks—from schema governance and automated release gates to experimentation pipelines and KPI/guardrail systems—that turn ambiguous product questions into executive-ready decisions. His cross-industry work includes incubation analytics at Pinterest that informed CEO-level investor narratives, forecasting and A/B testing during Albertsons’ COVID demand shocks, and founding a bank-wide analytics organization that delivered seven-figure savings and measurable productivity gains. As an adjunct lecturer at UC Berkeley, he teaches research design and maintains an open-source R tool for equitable virtual-classroom engagement, blending academic rigor with production discipline. Conor excels at bridging Product, Engineering, and Design to embed governed measurement into fast-moving platforms and to translate causal insights into strategic action.
7 years of coding experience
24 years of employment as a software developer
Bachelor of Science Psychology with a Math/Research emphasis, Bachelor of Science Psychology with a Math/Research emphasis at University of California, Davis
Master of Information and Data Science Data Science, Master of Information and Data Science Data Science at UC Berkeley School of Information
Certified Six Sigma Black Belt Lean Six Sigma, Certified Six Sigma Black Belt Lean Six Sigma at American Society for Quality (ASQ)
Master of Business Administration - MBA, Master of Business Administration - MBA at Western Governors University