Aurélien D.

End-to-end ML/AI product development within BBVA's Corporate & Investment Banking division. Building and maintaining Python/PySpark pipelines on AWS (SageMaker, Lambda, S3, Athena). Delivering ML models in a regulated banking environment with explainability and auditability constraints. Working in a hybrid Scrum team across the full ML lifecycle — from feature engineering to deployment and monitoring.

Scala/Spark pipeline development on IBM Cloud for credit risk data workflows in the banking sector (BNP Paribas / Cetelem). Implemented CI/CD automation via Jenkins and Git to standardize deployment across environments.

Designed and maintain a personal data engineering project for NBA fantasy basketball analytics. Built a Python-based ingestion engine pulling from public NBA APIs into a partitioned BigQuery data warehouse (70+ years of historical data). Automated daily ETL updates and model retraining via Airflow. Developed an XGBoost-based player scoring model with OOP-structured Python. Reduced BigQuery query costs by 35% through optimized table partitioning and clustering.