Summary
Josh Oberman is a data scientist with 11 years of experience who blends rigorous statistical foundations and the scientific method with practical machine learning applied at scale, most recently at Meta after a senior data science role at American Family Insurance. He excels at "gray area" problems that sit between mathematical models and business impact, with strengths in time series forecasting, experimental design, and production analytics. His background spans research-driven work at the University of Chicago, consulting implementations of VAR forecasting on cloud R pipelines, and ML roles in media and insurance, giving him a rare mix of academic rigor and production experience. Based in New York, he brings a pragmatic curiosity—evident in a concise GitHub ethos ("Everything is what it is, and not another thing")—that favors clear, auditable solutions over cleverness for its own sake.
11 years of coding experience
9 years of employment as a software developer
Coursera
Bachelor of Arts (B.A.) Philosophy and Allied Fields (Cognitive Science), Bachelor of Arts (B.A.) Philosophy and Allied Fields (Cognitive Science) at University of Chicago
French, English