Mateusz Sokół
Full-stack Software Developer at EY
Lower Silesian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolMateusz Sokół is a full-stack software developer with nine years of experience, currently building ASP.NET + Angular solutions at EY and previously developing Python systems at Nokia. He brings strong backend expertise in scientific Python libraries—contributing bug fixes, deprecation handling, and array API integration to high-profile projects like NumPy, SciPy, scikit-learn, and pandas. Mateusz has worked on performance- and correctness-sensitive tooling, including rule implementations for the Rust-based ruff linter to detect NumPy 2.0 deprecations, showing a careful eye for API evolution and cross-library compatibility. With an academic background in mechatronics and robotics from Politechnika Wrocławska, he pairs systems thinking with practical engineering, frequently adding tests and refactors that reduce technical debt across complex codebases.
9 years of coding experience
student, Mechatronics, Robotics, and Automation Engineering, student, Mechatronics, Robotics, and Automation Engineering at Politechnika Wrocławska
Stackoverflow
170reputation
3kreached
2answers
4questions
Github Skills (34)
algorithms10
static-code-analysis10
debug10
scipy10
python10
data-science10
ruff10
testing10
scikit10
pandas10
machine-learning10
numpy10
api10
scikit-learn10
linter10
Programming languages (17)
Github contributions (5)
numpy/numpy
Mar 2021 - Mar 2021
The fundamental package for scientific computing with Python.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:367 reviews, 2 commits, 107 PRs in 1 day
Contributions summary:Mateusz contributed to the core functionality of the NumPy library by identifying and fixing bugs related to the ``where`` keyword in the mean and variance methods. They also added test cases to ensure the fixes were correct and prevent regressions, and contributed to the deprecation of some libraries and functions. The user demonstrates expertise in the testing of mathematical operations, as well as code cleanup.
lapackpythonmpindarrayconvolution
scikit-learn/scikit-learn
Mar 2019 - May 2024
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:4 reviews, 8 PRs, 16 comments in 5 years 3 months
Contributions summary:Mateusz focused on enhancing array API support, specifically for the PCA algorithm, by integrating with array_api namespaces. Their contributions included adding array API checks and validation to ensure consistent behavior across different array implementations, like NumPy and CuPy. Further work involved adapting code for improved interoperability and utilizing array-based computations within the project, as well as removing outdated numpy functions. These changes demonstrate an understanding of the project's machine learning focus and its related libraries.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.