Engineering student at CentraleSupélec with a focus on data analytics, operations research, and industrial strategy. Currently completing a work-study contract at MBDA (defense sector) in supply chain & industrial strategy.

I'm drawn to roles that sit at the intersection of data science and business decision-making — not just building pipelines, but producing actionable insights and recommendations from data.

Languages & Tools: Python · SQL · Excel/VBA · Git · Power BI
Data Science: pandas · NumPy · scikit-learn · XGBoost · matplotlib · seaborn
Optimization: PuLP (MILP) · Linear Programming · Operations Research
Other: LaTeX · SharePoint · PowerPoint automation

Clustering ~200 retail stores using PCA and K-Means to optimize product assortment by store profile. Identified 3 clusters and built a data-driven assortment strategy (core vs. differentiated vs. excluded references).
Python scikit-learn K-Means PCA

ML project exploring whether socio-economic factors alone can predict academic success. Benchmarked 9 regression models and tackled class imbalance with SMOTE — turning a misleading 88.5% accuracy classifier into a useful early-warning tool (72% recall on at-risk students).
Python Random Forest SVR SMOTE XGBoost

A data pipeline for food waste redistribution — matching surplus inventory from retailers with local redistribution networks. Built as a portfolio project combining web scraping, data processing, and dashboard visualization.
Python GPT-4o Power BI Web Scraping

• CentraleSupélec — Engineering degree (grande école), specialization in supply chain & data science
• MBDA — Industrial strategy & supply chain (alternance, 2024–2026)
• Cartier (Le Locle) — Previous operational experience in luxury manufacturing
• President, CentraleSupélec Forum — Managed a €1M+ budget coordinating 200+ companies