Fundamental Commodity Researcher
📍 Location: London, UK | Hybrid (3–4 days in office)
đź‘Ą Team: Systematic Commodities Pod
About the Role
We are seeking a Fundamental Commodity Researcher to generate alpha and strengthen our understanding of global energy, metals, and agricultural markets.
You will design and maintain Python/SQL-based supply and demand models, develop systematic fair-value trading signals, and collaborate with quantitative researchers to integrate these insights into a live portfolio construction process.
We are looking for individuals who:
* Are energized by greenfield opportunities and building new frameworks from scratch
* Maintain a steep learning trajectory and enjoy working across diverse markets
* Combine attention to detail with an ability to deliver scalable, high-impact results
Key Responsibilities
* Build and maintain supply-demand balances across liquid energy, metals, and agricultural commodities
* Develop fair-value pricing models based on balances and related inputs
* Design and backtest systematic trading strategies informed by fair-value and balance data
* Enhance base balances using improved forecasting techniques and novel datasets
* Support portfolio-level hedging and positioning with a fundamental perspective
Resources & Support
You will be supported by a strong research and data infrastructure, including:
* Budget for third-party datasets, vendors, and external balance data to accelerate buildout
* A centralized data lakehouse and established data infrastructure
* Quant researchers to assist with signal design and modelling
* A quant developer to support large-scale technical builds
* Shared support for data onboarding, cleaning, and maintenance
Qualifications Required:
* 3+ years of experience in fundamental commodity research or supply-demand modelling at a hedge fund, bank, prop shop, or major physical merchant
* Deep knowledge of at least one major commodity sector (e.g., crude & products, base metals, grains)
* Strong programming skills in Python and SQL
* Proficiency in regression and time-series modelling techniques
* Proven ability to convert domain expertise into P&L-generating trade ideas
Preferred / Bonus:
* Experience collaborating with quantitative teams (e.g., feature engineering, model validation)
* Prior discretion over risk or advisory role to a trading book