Machine Learning Researcher - Systematic Commodities Hedge Fund
Core
Designing predictive models for cross-sectional and time-series commodity returns and turning ML ideas into live trading signals.
Role type
Applied ML specialist (systematic trading)
Builds
Production-ready ML models for global commodity futures trading
Domain
Financial markets / Systematic trading / Commodities
Deliverable
production ML models
Required skills
Python, statistical learning, model validation, feature engineering, time-series analysis, portfolio construction, risk modeling
Preferred skills
PhD in quantitative field, experience with LightGBM/XGBoost, deep learning, cloud/distributed compute, published research
Technologies
Python, LightGBM, XGBoost, deep learning frameworks
Responsibilities
Formulate and test research hypotheses using time-aware ML pipelines; Build and evaluate models (tree-based, linear, ensemble, deep learning); Run walk-forward and out-of-sample experiments; Analyze information coefficients, turnover, and risk-adjusted returns; Design feature engineering frameworks; Document findings and communicate results to portfolio managers
Seniority
Mid-Senior, hands-on IC