Since January 2023
Senior Software Engineer, then Staff Engineer
Owkin
Led multiple high-impact work streams and task forces with tight and business-critical deadlines.
Contributed to K-Pro, Owkin agentic copilot for biology and AI scientist for drug discovery.
- Led one of the four work streams to deliver a public SaaS, K-Pro Free, in 4 months.
- Improved interoperability of the product by leveraging the MCP protocol.
- Promoted security best practices (threat modelling, risk assessment matrix) for the application.
- Supported core teams on their architecture, design and implementation choices.
- Co-led tooling and CI efforts to improve developper experience and leverage AI coding tools.
Developed Substra, an open-source Federated Learning software hosted by the Linux Foundation.
- Maintained the Django backend and Python SDK, the golang orchestration component and the Kubernetes (Helm) packaging.
- Improved backend architecture, towards more isolation and flexibility, contributed various user-facing features, optimised performance across the stack.
- Restructured user and internal documentation following Diátaxis guidelines.
- Co-authored an internal white paper on Data Privacy and Federated Learning.
Coordinated an internal upskilling initiative to accompany the transition of 30 data scientists from research positions to product-delivering roles.
Delivered internal training for data scientists (on pipeline reproducibility, writing efficient tests, containerisation, …) and one-to-one technical mentoring.
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.