Software Engineer - Machine Learning

With a proven expertise in Machine Learning, I advocate and implement Software Engineering best practices and get companies ready to unlock the full potential of their Data Science models using the latest MLOps tools.

TL; DR:

12 years experience · Python · Data Science · Tech Lead · MLOps · Kubernetes · Technical Mentoring · Reproducible Pipelines · Software Architect · Open-Source · NLP · Data privacy · Ethical AI

Since January 2023

Senior Machine Learning Engineer

Owkin

Developed Substra, an open-source Federated Learning software.

• Contributed to the Python libraries, the Python / Django backend, the golang orchestration component and to the Kubernetes packaging.
• Improved backend architecture, towards more isolated components.
• Optimised performances when working on large datasets.
• Contributed to various user-facing features (experiment dependency management, supporting flexible compute plans to pave the way for advanced federated analytics).
• Restructured user documentation (following diátaxis guidelines).
• Worked on Kubernetes hardening of the applicative components.

Co-authored an internal white paper on Data Privacy and Federated Learning.

Delivered internal training for data scientists (on pipeline reproducibility, writing efficient tests…) and one-to-one technical mentoring.

Delivered an MVP for a malware scanner component on a critical timeline.

April 2021 - December 2022

Tech Lead - Credit modelling

Hokodo

Led the credit modelling team, implementing B2B short-term credit scores (BNPL).

• Maintained dozens of distinct models in production.
• Automated and streamlined modelling processes for reducing time-to-production.
• Prototyped, then delivered improved algorithms and feature selection techniques, while satisfying the industry requirements on explainability.

Set up modern MLOps and software engineering practices.

• Designed reproducible pipelines with DVC, model monitoring, end-to-end ownership on model implementation and deployment.
• Enforced code quality tools and processes (CI, unit tests, linting, reviews).
• Led the refactoring effort of the legacy codebase, all while delivering value for the business.
• Introduced the team to various important software engineering principles (SOLID principles,hexagonal architecture...).

Supervised a team of 4 Data Scientists and ML Engineers.

• Co-constructed the team technical roadmap, balancing technical and business constraints.
• Mentored the junior members in the team through code reviews, pair programming and one-to-one technical coaching.
• Organised coordination with the web engineering team and other stakeholders.

November 2017 - March 2021

Senior Data Scientist

PeopleDoc / UKG

Built an automated classification pipeline for HR documents, both text and scans, from scratch.

• Led the project from the POC phase to production-grade deployment, focusing on MLOps practices (execution performance, model monitoring, ...) and codebase maintainability.
• Designed the prediction API for integrating with other internal applications.
• Worked with the UI/UX team to improve results interpretability for end users.
• Created a POC to anonymise text data.

Built up and led a team of 5 persons.

• Hired, onboarded and mentored teammates (product owner, DevOps, ...).
• Wrote technical specifications and coordinated technical tasks.
• Championed a Machine Learning culture inside the company, including best practices around data privacy.

Developed MLV-tools, an open-source MLOps toolkit for easy Machine Learning pipeline versioning.

June 2016 - September 2017

Lead Data Scientist

WayKonect

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.

Tech Stack

• ML: pandas · NumPy · scikit-learn · PyTorch · skorch · Jupyter
• Code quality: pytest · Hypothesis · black · Data Version Control (DVC)
• DevOps: bash · git · Linux · Docker · Kubernetes · FluxCD · Argo Workflows
• Other programming languages: go · rust

Education & languages

• Engineering degree, ENSTA ParisTech, 2012
• Master of Science "Robotics, Cognition, Intelligence", TU München, 2012
• French (native speaker), English (C2), German (B2+)

Interests

• Open-source contributor
• Speaker at tech conferences (EuroPython, PyData, EuroSciPy, national PyCons)
• Mentoring in data science and public speaking