Lilian B

Paris, Ile-de-France

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Summary

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Rockstar

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Top School

Lilian B is an AI/ML engineer based in Paris with eight years of experience blending research-grade rigor and practical engineering across data science and machine learning tooling. Educated in mathematics and computer science at Université Paris-Saclay and earlier institutions, she brings a strong theoretical foundation to applied problems and has worked with teams including INRIA and JiminiAI. Her open-source contributions focus on improving the clarity, reproducibility, and pedagogical quality of ML code and docs—most notably on the scikit-learn MOOC and the data-prep library skrub—showing attention to detail and usability. Comfortable refactoring examples, fixing edge-case bugs, and adapting notebooks for reproducible workflows, she excels at turning complex ML concepts into accessible, production-ready artifacts. Colleagues would describe her as a meticulous communicator who values clear documentation as much as model performance.

8 years of coding experience

High School Diploma, High School Diploma at Saint-Erembert

Bachelor's degree Computer Science, Bachelor's degree Computer Science at Paris YNOV Campus

Master's degree Mathematics and Computer Science, Master's degree Mathematics and Computer Science at Université Paris-Saclay

Associate's degree Mathematics and Computer Science, Associate's degree Mathematics and Computer Science at INSTA

French, English, Russian, langues des signes

Github Skills (11)

scikit10

machine-learning10

python10

data-science10

documentation10

scikit-learn10

data-analysis9

dataprep9

data-preprocessing9

machine-learning-models8

data-cleaning8

Programming languages (7)

TypeScriptJavaShellPHPHTMLJupyter NotebookPython

Github contributions (5)

skrub-data/skrub

Oct 2020 - Jan 2023

Prepping tables for machine learning

Role in this project:

Data Scientist & ML Engineer

Contributions:6 releases, 374 reviews, 289 commits in 2 years 3 months

Contributions summary:Lilian made several commits focused on example code updates, specifically within a dimension reduction and performance analysis context. These changes included updating code within the examples of the library. Furthermore, the user refactored and addressed issues, such as a call within the project. The user also adapted the notebooks to use the fetching system within the project.

autoencoderdata-preprocessingdatadata-sciencetabular-data

INRIA/scikit-learn-mooc

Jan 2021 - Feb 2021

Machine learning in Python with scikit-learn MOOC

Role in this project:

Data Scientist

Contributions:7 reviews, 16 commits, 15 PRs in 1 month

Contributions summary:Lilian primarily focused on fixing typos and improving the clarity and consistency of the documentation and code within the repository. Their contributions involved correcting grammatical errors, standardizing code formatting, and ensuring alignment with exercise notebooks. The user's edits spanned multiple Python scripts related to machine learning concepts, demonstrating a focus on accuracy and readability within the context of the scikit-learn MOOC. They improved the quality and coherence of explanations in the context of the course.

pythondata-sciencemachine-learningscikit-learnmooc

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