Our client, a global asset management firm, are seeking a versatile, hands-on Data Scientist to join the Commodities Trading desk. You will work closely with traders, quantitative researchers, portfolio managers and engineers to design and deploy data-driven strategies, models and tools that improve trade selection, risk management, alpha generation and operational efficiency across physical and financial commodities (e.g., energy, metals, agriculture). This is a high-impact role requiring strong domain curiosity, solid quantitative skills, production-grade engineering, and the ability to communicate insights to non-technical stakeholders.
The role:
- Partner with traders and portfolio managers to identify trading opportunities and decision-making bottlenecks that can be addressed via data science and analytics.
- Develop, validate and deploy predictive models (statistical, ML, time-series) for forecasting prices, volatility, basis, seasonality, flows, inventories and other commodity-specific signals.
- Build regime-detection and market-state models to adapt strategies across different volatility/liquidity environments.
- Implement risk models and stress-test frameworks that quantify exposures (market, basis, carry, curve, roll) and assist real-time position limits and scenario analysis.
- Engineer real-time and batch data pipelines to ingest, clean, enrich and feature-engineer heterogeneous data sources (market data, fundamentals, trade blotters, order book, news, satellite/alternative data).
- Work with software engineers to productionize models and integrate them into trading systems, dashboards and order management workflows (backtesting, live signals, alerts).
- Perform rigorous model validation, backtests, performance attribution, and documentation for internal review and regulatory requirements.
- Monitor model performance, recalibrate as needed, and proactively detect model drift or data quality issues.
- Conduct bespoke research projects (alpha discovery, cost-of-trade / transaction cost analysis, optimal execution, statistical arbitrage across commodities) and present findings to stakeholders.
What you offer:
- Bachelor’s or Master’s degree in Quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, Financial Engineering, Physics, or equivalent). PhD preferred but not required.
- 4+ years of applied data science / quantitative modeling experience, ideally in commodities trading, energy markets, derivatives, or a related finance environment.
- Hands-on programming experience in Python (pandas, numpy, scikit-learn, statsmodels, PyTorch/TensorFlow desirable) and SQL for data manipulation and modeling.
- Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker) is a plus.
- Solid understanding of commodities market structure
- Excellent communication skills — able to present quantitative concepts and actionable recommendations to traders and senior management.
The sell:
- Collaborative, fast-paced environment with direct access to trading decision-makers
- Competitive compensation package with performance-linked incentives
- Work for a brand name that will help open doors for your career in the near future