Alexander Wang
Quantitative Trader
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolAlexander Wang is a quantitative trader in New York with seven years of experience applying computer science, math, and physics to high-frequency and options markets. He studied CS at Columbia, briefly pursued a CS MS at Stanford before leaving to launch a startup, and has since combined research-grade engineering with market-facing trading roles at SIG and Citadel Securities. As a co-founder incubated by Neo Accelerator, he brings product instincts alongside rigorous quantitative research proven in HFT, energy trading, and single-stock options. His open-source work on Featuretools shows attention to robust engineering—refactoring, improving error handling, and ensuring reliable feature serialization for automated feature engineering. Comfortable moving between low-level systems (C++, Linux) and data science tooling (Python, pandas, ML), he thrives on solving hard problems with creative, production-ready solutions.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Columbia UniversityDougherty Valley High SchoolMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Stanford University
Stackoverflow
11reputation
207reached
4answers
0questions
Github Skills (12)
machine-learning10
feature-engineering10
python10
data-science10
testing9
scikit8
scikit-learn8
api-design7
featuretools6
categorical-data6
dataframe6
onehot-encoding6
Programming languages (6)
Github contributions (5)
alteryx/featuretools
Jun 2019 - Aug 2019
An open source python library for automated feature engineering
Role in this project:
Data Scientist
Data ScientistContributions:6 commits, 15 PRs, 32 pushes in 2 months
Contributions summary:Alexander primarily focused on refactoring and improving the featuretools library. They removed deprecated functionalities, updated changelogs, and improved error messages, specifically addressing issues related to feature naming and schema versions. Their contributions involved testing and ensuring the correct behavior of the library, including the handling of feature serialization and dependencies. The user demonstrated expertise in maintaining the integrity and usability of the featuretools library.
feature-engineeringpythonmachine-learningdata-scienceautomated-machine-learning
alteryx/categorical_encoding
Jul 2019 - Aug 2019
Repository for the research and implementation of categorical encoding into a Featuretools-compatible Python library
Contributions:1 release, 90 commits, 14 pushes in 1 month
featuretoolspython
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