I currently serve as General Manager of Recommendations within Rakuten's AI Data Division, leading international teams building and operating large-scale recommendation systems. In parallel, I serve as Director of Search & Recommendations for Europe, leading multiple teams across the region.

At Rakuten, I helped turn recommendation from a small team running relatively simple systems into a multi-team engineering capability, while scaling served traffic by more than an order of magnitude. I built some of the early systems myself, then progressively moved from engineering and ML into architecture, product, team building, investment decisions and strategy — helping establish recommendation as a visible and trusted capability within the company.

My approach to technology is deliberately end-to-end. I like understanding a problem deeply enough to move across research, algorithms, software, infrastructure, operations and product, identify where the real leverage lies, and bring together the right expertise to take it further. As projects mature, my role naturally shifts from building the first version to creating the environment, organization and executive support needed to scale them.

Earlier in my career, I trained as an engineer at ESPCI Paris and worked in machine-learning research at INRIA, Université Paris-Sud and NeuroSpin. In 2010, I was part of the INRIA team that took leadership of scikit-learn and delivered its first public release ^[1]. It has since become one of the world's most widely used open-source machine-learning libraries.

Beyond scikit-learn, I published peer-reviewed work on machine learning for neuroimaging, including in IEEE Transactions on Medical Imaging and Pattern Recognition ^[2,3]. I later worked on data.bnf.fr, recognized with the Stanford Prize for Innovation in Research Libraries ^[4], and co-organized RecSysFR, the French recommender-systems community ^[5].

Today, my interests extend well beyond AI as a field in itself. I am particularly drawn to situations where software, data and AI can create or transform a real product or industrial capability — especially when the opportunity is still ambiguous, the path is not obvious, and there is something meaningful to explore, build and scale.

My role today increasingly combines technology strategy, team building and investment decisions. I stay close to engineering and product — testing early ideas, challenging assumptions, and helping teams turn what works into durable capabilities.