As data scientist and machine learning engineer at Schiphol, I developed forecasting models to improve insight into passenger flow across the airport, working with large volumes of operational data in Python using libraries such as pandas.
I owned the path from model development to production, including MLOps practices to make models reliable and maintainable in day-to-day operations. I used MLflow for experiment tracking, model versioning, and managing the transition from development to production, and worked with the team to deploy and operationalize models so they could support operational decision-making.