Florine Greciet
Lead Data Scientist
Montigny-sur-Vesle, Grand Est, France
Summary
🤩
Rockstar🎓
Top SchoolFlorine Greciet is a Lead Data Scientist with a PhD-level research background and about three years of industry experience transitioning complex statistical models into production-ready tools. Trained at ENSAI and through a doctoral thesis at Safran, she specializes in piecewise polynomial regression and physically informed models for industrial failure and lifetime prediction. She has led end-to-end projects—requirements, roadmaps, specs, development and production deployments—building Python Dash risk-mapping tools for insurers and public-sector clients. Comfortable across R and Python, she has contributed full‑stack improvements to the popular open-source Shapash explainability tool, focusing on UX and prediction-graph features. Her career blends rigorous academic modelling with pragmatic engineering: from developing combinatorial pricing heuristics for food-waste platforms to migrating SAS analytics into scalable Python pipelines. Based in Montigny-sur-Vesle, she combines hands‑on coding, product design, and cross‑team leadership to deliver interpretable, production-grade ML for regulated industries.
3 years of coding experience
5 years of employment as a software developer
Thèse de doctorat, Mathématiques, Thèse de doctorat, Mathématiques at Universite de Lorrainem2, Mathematical Statistics and Probability, informatics and economics, m2, Mathematical Statistics and Probability, informatics and economics at ENSAIlicence mass, mathématiques, statistiques, informatiques, sciences cognitives, licence mass, mathématiques, statistiques, informatiques, sciences cognitives at Université de Bordeaux
English
Github Skills (9)
dashjs10
python10
plotly10
explainable-artificial-intelligence8
feature-selection8
html6
css6
machine-learning6
javascript4
Programming languages (1)
Github contributions (1)
MAIF/shapash
Sep 2022 - Oct 2022
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:5 reviews, 37 commits, 10 pushes in 1 month
Contributions summary:Florine's commits primarily focus on enhancing the Shapash web application. They are responsible for creating and updating tabs within the application, and improving the responsiveness of the navigation bar. They also worked on correcting hovertext bugs, optimizing the color, font, and size of the UI elements, and refining the layout for improved usability. Additionally, they are implementing functionality related to the prediction graph.
explainable-mlmachine-learning-modelsimlpythontransparent
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