Summary
Mehdi Abedi is a data scientist in the San Francisco Bay Area with 9 years of cross-disciplinary experience and 3+ years focused on experimentation, causal inference, and ML for product and healthcare outcomes. He builds robust ETL and Databricks pipelines, designs well-powered A/B tests, and turns ambiguous questions into actionable metrics that have driven measurable impact—reducing reporting errors, readmissions, and healthcare costs while improving retention and product KPIs. Comfortable across Python, SQL, Spark, XGBoost, SHAP, and Power BI, he blends engineering, econometrics, and cognitive science to create interpretable models and feature sets informed by behavioral theory. Equally at home shipping full-stack projects and Chrome extensions on GitHub, he pairs pragmatic production engineering with a curiosity-driven exploration of LLM and web tooling.
9 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Applied Econometrics (Statistics), Master of Science - MS, Applied Econometrics (Statistics) at University of San Francisco
Master of Science - MS, Cognitive Science and Anthropology, Master of Science - MS, Cognitive Science and Anthropology at University of Tehran