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