Victor Sheng is an experienced data scientist and advisor with 14 years blending quantitative finance, product management, and applied ML across firms from Bank of America Merrill Lynch to Elevance Health and YC-backed Hive Health. He has deep domain expertise in counterparty portfolio management—helping optimize collateral, initial margin and regulatory capital—and has translated that into technical product and analytics infrastructure on IA tech teams. Victor pairs rigorous academic training (MS in Data Science, Harvard; BS in Financial Engineering, Columbia) with hands-on engineering, evidenced by backend contributions to an open-source Telegram library. Comfortable moving between research, production ML, and trading-floor constraints, he’s known for turning complex regulatory and risk problems into pragmatic, auditable solutions. Based in Mountain View, he brings a rare mix of quant modeling, product ownership, and open-source backend development.
14 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Data Science, 3.93/4.00, Master of Science - MS, Data Science, 3.93/4.00 at Harvard University
Bachelor of Science - BS, Operations Research: Financial Engineering, 3.85/4.00 magna cum laude, Bachelor of Science - BS, Operations Research: Financial Engineering, 3.85/4.00 magna cum laude at Columbia University in the City of New York
Contributions:1 release, 156 commits, 34 PRs in 1 year 1 month
Contributions summary:Victor appears to be focused on building the backend for the Telegram library, as suggested by the commit messages "tgl: initial commit" and the code changes in `binlog.c`, `queries.c` and `structures.c`. Their work includes implementing core functionalities such as setting up the core data structures. They have also made contributions to the functionality for secret chats, which suggests a deeper involvement in the core features of the library.
Contributions:9 commits, 3 PRs, 5 pushes in 6 years 2 months
schemeparsingtloparser-generatorparser
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