I'm good at Machine Learning, Python, Natural Language Processing, pandas, NumPy, PyTorch, scikit-learn, French, English
I'm proficient in pytest, git, Linux CLI, Computer Vision, data viz, DVC (Data Version Control), ML interpretability, German
I'm dabbling in bash, Ansible, SQL, Flask, sphinx, html/css, Python packaging
I know a bit about task queues, Docker, Kubernetes
I care about algorithmic fairness, data privacy, ML pipeline explainability, code quality, DataOps
Built from scratch an automated classification pipeline for HR documents, all formats.
Set up the Machine Learning team in the company.
Developed MLV-tools, an open-source toolkit to version easily Machine Learning pipelines.
Developed data-driven algorithms for connected cars.
Implemented the data pipeline from scratch, with a focus on code quality and reproducibility of results.
Designed the corporate data strategy to improve data gathering in the long term.
Developed a personalised coaching algorithm to improve driver safety and promote eco-driving.
Research and development on a rule inference engine (quality analysis on manufacturing processes).
Refactored legacy code, updated documentation and tests, improved rule intelligibility.
Improved predictive power of the rule engine using boosting techniques.
Added a prescriptive module for correcting poor quality outcomes.
6-month internship: Inference of gene regulatory networks from DNA chips data
Implemented in Python optimisation algorithms for Deep Neural Networks.