Summary
Chris Salahub is a data scientist with a PhD in Statistics and 11 years of experience building production analytics and modeling solutions across industry and academia. Based in Chicago, he has delivered end-to-end Google Cloud systems combining search, survey, and text data into interactive dashboards, implemented streaming monitoring pipelines, and paired LLM calls with classical NLP for insight extraction. His background spans measurement science, demographic microsimulation, and experimental design—work that has reduced simulation convergence times and virtually eliminated panel over-recruitment. A practiced instructor and consultant, he translates complex statistical methods (Poisson, Cox, time‑series outlier detection, GLMs) into reliable production code in Python, R, and SQL. Curious by nature, he combines deep methodological training from Waterloo and ETH Zürich with a knack for turning academic techniques into practical business impact.
11 years of coding experience
2 years of employment as a software developer
Master of Science (M.Sc.), Statistics, Master of Science (M.Sc.), Statistics at ETH Zürich
Doctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at University of Waterloo
English, German