I did not start out planning to become a data scientist. I studied agronomy engineering at Institut Agro - Agrocampus Ouest in Rennes, which is fundamentally about applying science to complex living systems, and during my statistics courses I realised that the analytical side was what excited me most. Not just running models, but understanding why a method works, and what it can and cannot tell you about the real world.
After finishing both my engineering degree and a master's in applied mathematics and statistics in 2021, I joined Astek / IT&M Stats, a consultancy that places statisticians and data scientists inside large R&D organisations. That structure shaped how I work: missions ranging from a few months to nearly two years, each bringing a new domain, a new team, and a new set of constraints. It forces you to get good at context-switching, at earning trust quickly, and at building things that still work when you are not in the room to explain them.
My first mission was at L'Oréal R&I: two years of clinical biostatistics on ingredient efficacy and molecular safety studies. I covered hundreds of analyses, learned what a two-week turnaround really means under pressure, and built internal tooling that helped the team move faster. It was a good introduction to regulated science, the kind of work where you cannot hand-wave uncertainty.
Then Sanofi R&D, where I shifted from pure analysis to building. I designed and shipped a full R Shiny platform to predict and simulate manufacturing plant resource capacity across global sites, driving drug production planning and decision-making, while also taking on the Scrum Master role. That project taught me that useful software is harder to build than merely correct software, and that documentation, workshops, UAT, and handover are part of engineering rather than tasks that happen after it.
After Sanofi came Abolis, a microbiome biotech startup. Six months, full autonomy, multi-omics data pipelines, no hand-holding. Very different energy from a pharma company, and a useful reminder that speed only helps when the foundations are still clear.
Now I am at Chanel Parfums Beauté R&D, working on machine learning, R Shiny applications, and internal data products for fragrance and cosmetics development. Still on mission via Astek / IT&M Stats, but it is the most technically ambitious role so far: production models, app catalogs, platform tooling, and software that scientists depend on daily.
Outside work, I follow One Piece with unreasonable commitment, still make time for video games when I can, and have a lot of time for people who can say "I was wrong about that" and simply update their view.