David聽Quiroz
Machine Learning Engineer at :probabl.
Paris, Ile-de-France
Summary
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Rockstar馃帗
Top SchoolArturo Amor is a Machine Learning Engineer with a PhD in physics and four years of professional experience. He works at :probabl. and is a core contributor to scikit-learn, where he focuses on improving model stability and API validation. His open-source work includes tightening Ridge alphas validation and backward-compatibility checks, enhancing dataset documentation, and refining scikit-learn MOOC notebooks for clearer, reproducible teaching examples. Based in Mexico, he brings a physics-trained attention to numerical rigor and pedagogy, bridging research-grade correctness with production-ready ML engineering.
4 years of coding experience
5 years of employment as a software developer
Universidad Nacional Aut贸noma de M茅xico (UNAM)Postdoc, Theoretical and Mathematical Physics, Postdoc, Theoretical and Mathematical Physics at 脡cole Polytechnique
Spanish, English, French
Github Skills (10)
scikit-learn10
data-analysis10
jupyter-notebook10
machine-learning10
documentations10
python10
documentation10
scikit10
model-validation9
linear-regression8
Programming languages (4)
Github contributions (5)
INRIA/scikit-learn-mooc
Jul 2021 - Oct 2022
Machine learning in Python with scikit-learn MOOC
Role in this project:
Data Scientist
Data ScientistContributions:156 reviews, 108 commits, 211 PRs in 1 year 3 months
Contributions summary:David primarily contributed to the improvement and update of existing notebooks related to machine learning concepts. The changes involved fixing grammatical errors, refining wording, and correcting typos in the notebook content. Additionally, the user updated notebook examples to be consistent and reflect best practices.
pythondata-sciencemachine-learningscikit-learnmooc
scikit-learn/scikit-learn
Nov 2021 - Jan 2023
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:550 reviews, 47 commits, 150 PRs in 1 year 2 months
Contributions summary:David's contributions primarily involve modifications to the scikit-learn library related to machine learning models and documentation. They improved the handling of the `alphas` parameter in Ridge-related models, including validation and backward compatibility checks. They also worked on documentation, ensuring proper use of numpydoc validation for dataset loading functions and adding a minimal reproducer guide. The user's involvement is centered on improving model stability, validation procedures, and clarity of the library's documentation.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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