Brian Wignall
Head Of Portfolio Management at Hedge Fund
New York City Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolBrian Wignall is a Head of Portfolio Management based in the NYC metro area with 12 years of experience building and running quantitative investment strategies across hedge funds and institutional asset managers. He combines a PhD-level grounding in statistics from UC Santa Barbara with hands-on quant research and portfolio leadership roles at Two Sigma, Acrisure Capital, and Edgestream, rising to CIO-level responsibilities. Equally comfortable in code and math, he contributes to major open-source projects—including the Glasgow Haskell Compiler and scikit-learn—where he’s improved compiler internals, ML linear-model features, and testing quality. Known for meticulous documentation and test-driven improvements, he brings a rare blend of production-grade software craftsmanship to portfolio construction and risk management.
12 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at UC Santa Barbara
Github Skills (30)
test-automation10
data-analysis10
python10
testing10
scikit10
pandas10
machine-learning10
internals10
compiler-design10
ghc10
scikit-learn10
functional-programming10
haskell10
documentation10
debug9
Programming languages (24)
Github contributions (5)
pandas-dev/pandas
Jan 2014 - Dec 2019
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:38 commits, 4 PRs, 1 comment in 5 years 11 months
Contributions summary:Brian's commits primarily focused on refactoring and specializing existing test assertions within the pandas library. They replaced general assertion calls with more specific forms such as assertEqual, improving the clarity and maintainability of the test suite. The user's work involved changes across multiple test files, demonstrating a broad understanding of the testing infrastructure and a commitment to code quality. They also corrected minor spelling errors in documentation files.
pythondatalabeled-datamanipulationdataframes
scikit-learn/scikit-learn
Jan 2014 - Dec 2019
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:12 commits, 2 PRs, 3 comments in 6 years
Contributions summary:Brian contributed to the `scikit-learn` repository by implementing and testing new functionality related to linear models, specifically focusing on the LassoCV and ElasticNetCV models. Their work included adding options to restrict coefficients to positive values and writing tests to ensure these constraints function correctly. They also fixed minor typos in the documentation.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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