Maren Westermann
Senior Machine Learning Engineer at scikit-learn
Berlin, Germany
Summary
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Rockstar🎓
Top SchoolMaren Westermann is a Senior Machine Learning Engineer based in Berlin with a decade of applied ML experience and over 15 years working across the full data lifecycle—from collection and quality assurance to model building, deployment and monitoring. She combines a strong scientific background (PhD in Soil Sciences) with industry experience building AI agents and production ML systems for Deutsche Bahn, FREE NOW and health and agriculture projects, bringing domain-driven modelling to large, operational contexts. An active open-source contributor and scikit-learn team member, she focuses on improving documentation and newcomer onboarding for one of the field’s most-used libraries. As a PyLadies Berlin co-organiser she pairs technical leadership with community building and inclusive mentorship, and she brings a consistent interest in sustainability and practical open-source solutions to her engineering work.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Soil Sciences, Doctor of Philosophy - PhD, Soil Sciences at The University of QueenslandMaster's degree, Biology, General, Master's degree, Biology, General at Justus Liebig University Giessen
Stackoverflow
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Github Skills (8)
scikit-learn10
documentation10
user-guide10
scikit10
data-science9
data-analysis9
machine-learning9
python8
Programming languages (7)
Github contributions (5)
scikit-learn/scikit-learn
Mar 2020 - Jan 2023
scikit-learn: machine learning in Python
Role in this project:
Technical Writer
Technical WriterContributions:74 reviews, 34 commits, 48 PRs in 2 years 10 months
Contributions summary:Maren primarily contributed to the project by adding and improving documentation. Their work included adding examples, shortening links, and correcting documentation for various modules, including those related to naive Bayes, partial least squares, and Gaussian processes. The commits also involved fixing linting errors and updating documentation related to the `coef_` and `intercept_` attributes in several classifiers and the `n_nonzero_coefs_` attribute. Overall, the user focused on enhancing the clarity, accuracy, and accessibility of the project's documentation.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
marenwestermann/pandas
Aug 2022 - Mar 2024
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Contributions:28 pushes, 8 branches in 1 year 7 months
polarspythondatalabeled-datamanipulation
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