Angelina Pan
Quantitative Strategy Associate
New York City Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolAngelina Pan is a Quantitative Strategy Associate at Goldman Sachs with eight years of technical experience bridging physics, computer science, and finance. Trained at Caltech, she moved from computer vision research and teaching into quantitative roles, applying rigorous academic methods to real-world trading and securities strategies. At Goldman Sachs she progressed from summer analyst to analyst and now associate, contributing to model development and data-driven decision making in the New York City metro area. Her background includes internships at Google and research stints at the University of Toronto, reflecting a blend of industry-scale engineering and academic inquiry. Known for translating complex algorithms into production-ready tools, she brings strong analytical rigor and a knack for interdisciplinary problem solving. Outside of finance, her trajectory suggests a persistent curiosity in machine learning applications beyond standard quant workflows.
8 years of coding experience
3 years of employment as a software developer
California Institute of TechnologyHigh School Diploma, High School Diploma at Havergal College
English, French, Chinese, Latin
Github Skills (2)
aris8
python4
Programming languages (1)
Github contributions (5)
eqian99/GeneratePoetry
Mar 2019 - Mar 2019
Contributions:8 pushes in 2 days
gvanhorn38/fish_eye
Mar 2019 - Jul 2020
Analyzing ARIS data to detect, track, and count fish.
Contributions:28 commits, 19 pushes in 1 year 4 months
analyzingpythonarisdetectfish
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