Jerry Xin is a Senior Data Scientist and Quantitative Analyst with 12 years of experience applying machine learning, causal inference, and statistical modeling to financial services and healthcare problems. Currently at J.P. Morgan Chase, he blends LLM prompt engineering, tree-based models, and propensity score matching to extract drivers of customer satisfaction and optimize digital product experiments. His background spans payment error measurement for Medicare/Medicaid, STIRS options market making, and academic research in entity resolution and Bayesian reinforcement learning at Duke. He has hands-on production experience across Python, SQL, Snowflake, Alteryx, SAS, and XGBoost, and has contributed an entity-resolution Python package. Comfortable moving between research and production, he’s notable for translating advanced causal and ML methods into actionable product insights for large-scale, regulated environments.
12 years of coding experience
3 years of employment as a software developer
Master of Science, Artificial Intelligence, Master of Science, Artificial Intelligence at The University of Texas at Austin
Bachelor of Science - BS, Computer Science, Statistical Science, Bachelor of Science - BS, Computer Science, Statistical Science at Duke University
Valedictorian: Rank 1/739
, Valedictorian: Rank 1/739
at Desert Vista High School
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