David Quiroz
Machine Learning Engineer at :probabl.
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolDavid Quiroz is a Machine Learning Engineer and co-founder at :probabl. with a PhD in theoretical quantum physics from UNAM and four years of industry experience bridging rigorous research and production ML. He is a core contributor to scikit-learn—improving model stability, parameter validation, and documentation—and maintained the popular "Machine learning in Python with scikit-learn" MOOC while at Inria. His background in theoretical and computational physics informs a meticulous, model-first approach to algorithm design and reproducible workflows. Based in Paris, he combines academic credentials (SNI-CONACyT membership) with startup pragmatism, and is trilingual in Spanish, English (C1) and French (B2).
5 years of coding experience
5 years of employment as a software developer
Universidad Nacional Autónoma de México (UNAM)Postdoc, Theoretical and Mathematical Physics, Postdoc, Theoretical and Mathematical Physics at École Polytechnique
Spanish, English, French
Github Skills (9)
scikit-learn10
scikit10
machine-learning10
jupyter-notebook10
python10
documentation10
data-analysis10
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:192 reviews, 108 commits, 231 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.
machine-learningpythonscikit-learnmooc
scikit-learn/scikit-learn
Nov 2021 - Jan 2023
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:619 reviews, 47 commits, 179 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.
machine-learningpythonscikit-learnstatisticsdata-science
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