Sergey Feldman
Head Of AI at Data Cowboys
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSergey Feldman is a seasoned AI leader with 13 years of experience building and deploying machine learning systems across research labs, consultancies, and startups from Seattle. As Head of AI at Alongside and a founding partner of boutique consultancy Data Cowboys, he blends hands-on algorithm design with client-facing productization, including deep learning for NLP and large-scale health data projects. His research roots (PhD, University of Washington) and long tenure at AI2 inform pragmatic, publication-quality solutions, and he has contributed to widely used open-source projects like scikit-learn and fancyimpute—improving QDA, covariance regularization, and multiple-imputation methods. Colleagues describe him as someone who moves fluid academic ideas into production-ready tools while keeping communication clear for non-technical stakeholders.
13 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD) Electrical and Electronics Engineering, Doctor of Philosophy (PhD) Electrical and Electronics Engineering at University of WashingtonBachelor of Science (BS) Electrical and Electronics Engineering, Bachelor of Science (BS) Electrical and Electronics Engineering at University of Illinois Chicago
Russian
Github Skills (10)
scikit10
statistics10
machine-learning10
python10
data-science10
numpy10
scikit-learn10
linear-algebra8
algorithm7
algorithms7
Programming languages (8)
Github contributions (5)
iskandr/fancyimpute
Dec 2015 - Oct 2021
Multivariate imputation and matrix completion algorithms implemented in Python
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 60 commits, 42 PRs in 5 years 10 months
Contributions summary:Sergey contributed significantly to the `fancyimpute` repository by implementing and refining imputation algorithms. Their work included the addition of Bayesian Ridge Regression and MICE (Multiple Imputation by Chained Equations) with both row and column-based imputation methods, suggesting a focus on statistical modeling and missing data handling. These changes enhanced the library's capabilities in handling missing data in datasets, expanding its utility for various data science applications. They also refactored some variable names to improve code readability.
imputationpythonmatrixmultivariatecompletion
scikit-learn/scikit-learn
Jul 2013 - Sep 2019
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:15 commits, 53 PRs, 631 comments in 6 years 3 months
Contributions summary:Sergey's commits primarily focus on modifying the `sklearn/qda.py` file, indicating their involvement in the quadratic discriminant analysis (QDA) implementation within the scikit-learn library. They introduced covariance regularization, updated the code to meet PEP8 compliance, and ensured the `reg_param` was correctly handled. The contributions also include updates to the example code in `test_qda.py`.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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