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 machine learning researcher (systematic trading)
Builds
Production-ready ML models and portfolio-level forecasts for global commodity futures
Domain
Systematic trading / Commodities / Financial markets
Deliverable
production ML models
Required skills
Python, statistical learning, model validation, feature engineering, time-series analysis, ensemble methods, deep learning, portfolio construction, risk modeling
Preferred skills
PhD in quantitative field, financial markets experience, cloud/distributed compute, published research
Technologies
LightGBM, XGBoost, deep learning frameworks, scientific computing stacks
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 with realistic costs; Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns; Design feature engineering frameworks and reusable research tooling; Translate research ideas into production-ready implementations; Collaborate with engineers to deploy models into live trading systems
Seniority
Mid-Senior, hands-on IC