Antoine Baker
Full-stack Developer at Eastdil Secured
Charlotte, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolAntoine Baker is a full-stack developer based in Charlotte, NC with 11 years of professional experience building and maintaining enterprise web applications. He currently drives full-stack initiatives at Eastdil Secured after nearly a decade of application development and programming roles that grounded him in practical, production-grade systems. Beyond web engineering, Antoine has contributed to the widely used scikit-learn library, improving sample-weight handling and cross-validation robustness—an uncommon intersection of ML algorithmic rigor and backend engineering. He combines hands-on coding across the stack with careful testing and refactoring practices that emphasize numerical stability and correctness, even for sparse or rank-deficient data. Known for steady long-term engagement, he brings both institutional knowledge and an engineer’s curiosity to bridge data science needs with reliable application delivery.
11 years of coding experience
11 years of employment as a software developer
Bachelor of Science (B.S.), Computer Information Systems, Bachelor of Science (B.S.), Computer Information Systems at Gardner-Webb UniversityAssociate of Science (A.S.), Computer Programming, Associate of Science (A.S.), Computer Programming at Isothermal Community College
Github Skills (14)
scikit-learn10
statistics10
machine-learning10
python10
data-science10
scikit10
testing9
linear-regression8
numpy8
cross-validation7
sparse-matrix7
variable-selection6
feature-selection6
multiple-selection6
Programming languages (2)
Github contributions (5)
scikit-learn/scikit-learn
May 2024 - Apr 2025
scikit-learn: machine learning in Python
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:81 reviews, 12 PRs, 123 comments in 11 months
Contributions summary:Antoine contributed to the scikit-learn library, focusing on improving the handling of sample weights and cross-validation within various estimators. Their work involved refactoring existing code to ensure that sample weights are correctly propagated through different cross-validation schemes and that the equivalence between sample weighting and data repetition is maintained. They also added checks and tests to ensure the library's robustness and numerical stability, particularly for sparse data and rank-deficient cases. This user's contributions are primarily focused on the machine learning algorithms and improving their behavior.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
sphinxteam/tramp_notebooks
Jan 2019 - Apr 2020
Contributions:19 commits, 3 pushes in 1 year 2 months
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