Jing Wu - Research Lead at Point72

Jing Wu

Research Lead at Point72

New York, New York, United States

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Summary

👤

Senior

🎓

Top School

Jing Wu is a Research Lead and Ph.D. candidate in Statistics at Columbia University with roughly a decade of experience applying machine learning, Bayesian methods, point processes, and network analysis to quantitative problems. At Point72 she leads research in quant equity, translating advanced statistical models into trading insights, and previously interned at Apple and Morgan Stanley where she honed applied data science skills. Her academic work under Professor Tian Zheng complements hands-on contributions to Columbia’s Applied Data Science course projects—where she clarified OCR evaluation methods and documentation—highlighting a knack for communicating complex analyses. Comfortable bridging academia and industry, she combines rigorous statistical training from Nankai and Columbia with practical experience shipping data-driven solutions in high-stakes environments.

10 years of coding experience

Bachelor of Science (BS) Statistics, Bachelor of Science (BS) Statistics at Nankai University

Doctor of Philosophy (Ph.D.) Statistics, Doctor of Philosophy (Ph.D.) Statistics at Columbia University

Github Skills (5)

ocr10

optical-character-recognition10

documentation10

data-analysis9

r-programming9

Programming languages (2)

HTMLJupyter Notebook

Github contributions (5)

TZstatsADS/ADS_Teaching

Aug 2018 - Nov 2018

Teaching repo for Applied Data Science @ Columbia, a project-based course for data science skills (statistical thinking, machine learning, data engineering, team work, presentation, endurance of frustration, etc).

Role in this project:

Data Scientist

Contributions:29 commits, 23 pushes in 3 months

Contributions summary:Jing's contributions primarily involve updates to documentation and the addition of summaries related to a project focused on Optical Character Recognition (OCR). They appear to be involved in the project's analysis and presentation. This is evident from the code differences focusing on the description of the OCR process, and the inclusion of evaluations based on word and character levels of analysis. The changes suggest a focus on explaining and summarizing the methodology and results within the repository.

data-engineeringdata-sciencemachine-learning

wendy9217/Kaggle2016Summer

Jun 2016 - Sep 2018

Contributions:1 push, 1 branch, 1 comment in 2 years 3 months

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