Seyed Jalil Kazemitabar is an expert in applied micro-modeling for causal inference and predictive analysis. Jalil develops data analytics infrastructure at Scientific Revenue, where he is in charge of creating and maintaining predictive analytics solutions to identify user value.
Jalil was an economics research and teaching assistant at UC Berkeley, working among the field's leading contributors in economics research and development. During this tenure, he collaborated with health economists and developed analytics solutions to evaluate risk hazards of anti-diabetic medicines.
A specialist in applied econometrics and applied machine learning, Jalil merges his extraordinary understanding of economics with solution based software engineering. Jalil has his BSc in Mathematics from Sharif University of Technology, and M.A. in Economics from UC Berkeley.