Lucy Wu is a data scientist with eight years of experience blending rigorous mathematics, finance, and statistics from the University of Pennsylvania with hands-on product work at Facebook/Instagram focused on feed ranking and growth. She has interned across quantitative finance and ML-focused teams—Quantopian, Draper, and PrecisionLender—where she contributed to public quantitative research materials and built models for trading, loan risk, and user behavior. As a Wharton TA she translated advanced data mining and investment concepts into teachable material, and her GitHub contributions include polished exercises for the widely used Quantopian research lectures. Based in New York, she combines academic grounding with practical model deployment in high-impact consumer and financial settings, and she has a knack for turning educational content into clearer, production-ready artifacts.
8 years of coding experience
North Carolina School of Science and Mathematics
Bachelor of Science - BS, Economics: Finance and Statistics, Bachelor of Science - BS, Economics: Finance and Statistics at The Wharton School
Bachelor of Arts - BA, Mathematics, Bachelor of Arts - BA, Mathematics at University of Pennsylvania
Contributions:7 commits, 3 PRs, 5 pushes in 13 days
Contributions summary:Lucy contributed exercises and answer keys related to linear correlation analysis and introductory futures contracts, which are part of the Quantopian lecture series. They added formatted questions and answers, specifically within the context of the Introduction to pandas lecture. Furthermore, they fixed formatting issues in the existing content, enhancing its presentation.
Contributions:60 pushes, 4 branches in 1 year 10 months
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