Machine Learning Specialist

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

Since November 2017

Machine Learning Engineer


Built from scratch an automated classification pipeline for HR documents, all formats.

  • Improved HR users day-to-day workflow, and reduced error rates.
  • Facilitated new customer implementation to the internal data model.
  • Validated technical feasibility through a POC model working both on text and on scan (images).
  • Productionised the code and improved execution performances.
  • Designed the prediction API for integrating with other internal applications.

Set up the Machine Learning team in the company.

  • Promoted a Machine Learning culture inside the company, through demos, talks and workshops.
  • Supervised the development of an internal ML platform.
  • Initiated a ML mindset in different departments (hardware requirements, data access, ...).

Developed MLV-tools, an open-source toolkit to version easily Machine Learning pipelines.

June 2016 - September 2017

Lead Data Scientist


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.

May 2012 - June 2016

Machine Learning Research Engineer

Dassault Systèmes

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

October 2011 - April 2012

Research assistant

TU München / DLR

Implemented in Python optimisation algorithms for Deep Neural Networks.


  • Master of Science "Robotics, Cognition, Intelligence", TU München, 2012
  • Engineering degree, ENSTA ParisTech, 2012


  • Open-source contributor
  • Speaker at tech conferences (EuroPython, PyData, EuroSciPy, national PyCons)
  • Horseback archer competing at international level