Senior Data Scientist - Commodity Trading and Risk Management

McKinsey is seeking a Senior Data Scientist to join its Commodity Analytics team in Amsterdam, Brussels, Lisbon, or Madrid. The role focuses on applying advanced analytics and mathematical modeling to enable superior commodity risk management and trading decisions for global clients across agribusiness, metals, energy, and consumer food sectors. Key responsibilities include building, re-training, and expanding commodity hedging strategies, developing price forecasting models, and modifying algorithmic trading models to perform across diverse market regimes. Additionally, the Senior Data Scientist will design and backtest risk management strategies, integrate workflows with CTRM/ETRM systems via APIs, and collaborate with client teams to scale analytical capabilities. The ideal candidate possesses a strong quantitative background and is proficient in production-quality Python. Technical requirements include experience with time-series analysis of mean-reverting and cointegrated prices (ARIMA, VECM, GARCH), supervised learning (SVM, LSTM, gradient boosting), unsupervised learning (PCA, K-means), mathematical optimization (LP/MILP, stochastic models), and NLP/LLM-based workflows. Familiarity with pytest, reproducible workflows, CI/CD, and scikit-learn is required, alongside experience in physical or cash commodity markets.