Machine learning and quantitative projects built past the notebook stage.

Paris-Dauphine finance and machine learning project

Loan approval scoring pipeline from experimentation to batch inference

Refactored an academic credit scoring study into a modular Python project with training, feature engineering, batch inference, business-facing risk bands, and Docker packaging.

Advanced unsupervised learning and audio representation project

Unsupervised audio pipeline with handcrafted features and Transformer embeddings

Built a full unsupervised audio clustering workflow on 769 environmental clips, then benchmarked classical acoustic descriptors against AST, wav2vec2, and HuBERT embeddings.

Paris-Dauphine analytics and data visualization project

Clustering, profiling, and dashboarding for marketing decisions

Performed unsupervised customer segmentation on 2,240 clients, compared clustering families, and packaged the results in a Shiny dashboard for business exploration.

Applied reinforcement learning project inspired by a medical control paper

Reinforcement learning for adaptive treatment policies under constraints

Implemented a reinforcement learning controller for chemotherapy dosing, simulated several patient profiles, and reproduced a robustness study over 15 synthetic patients.

Time series and actuarial applications project

ARIMA, GARCH, ARIMAX, VAR, and causal analysis across market and macro data

Built an end-to-end econometrics workflow on L'Oreal stock prices and French macroeconomic series, covering stationarity, forecasting, volatility modeling, and multivariate dynamics.

Applied machine learning extension of an academic GLM project

Model comparison platform for operational demand forecasting

Built a forecasting workflow for bike rental demand with six models, Box-Cox target transformation, model tracking, and deployment-ready packaging.