Commodity Quantitative Analyst

Camber Morris are looking for a brilliant Commodity Quantitative Analyst to join a top tier macro hedge fund in London. Joining our elite macro hedge fund team in London, you will step into a high-impact role where your mathematical models and systematic strategies will directly drive investment decisions. Operating on a hybrid model that balances collaborative in-office strategy sessions with remote focus time, this permanent position offers the chance to sit at the absolute intersection of advanced quantitative research and real-world macroeconomic shifts.

Key Responsibilities

• Model Development & Optimization: Design, build, and implement sophisticated mathematical models to price, analyse, and trade across a diverse range of commodity markets (energy preferred).
• Alpha Generation: Formulate, backtest, and refine systematic trading strategies, translating complex data sets into highly profitable, risk-adjusted trading signals.
• Alternative Data Exploration: Source, clean, and structure non-traditional data sets—such as shipping telemetry, satellite imagery, and weather patterns—to gain a predictive edge in physical commodity flows.
• Portfolio Risk Analysis: Collaborating closely with portfolio managers, develop robust risk management frameworks to stress-test positions against macroeconomic shocks and liquidity constraints.
• Infrastructure Advancement: Enhance Camber Morris's proprietary quantitative research platform, ensuring our codebase is scalable, fast, and optimised for real-time market analysis.

Required Skills & Experience

• Advanced Academic Background: A Master’s degree or PhD in a highly quantitative discipline, such as Mathematics, Physics, Quantitative Finance, or Computer Science.
• Programming Mastery: Exceptional coding skills in Python, C++, or R, with a proven track record of writing clean, production-grade code and utilising advanced data science libraries.
• Commodities Expertise: At least 3 years of experience as a quantitative analyst within a hedge fund, proprietary trading firm, or investment bank, with a deep understanding of commodity derivatives, curves, and physical market dynamics.
• Statistical Rigor: Strong knowledge of time-series analysis, machine learning algorithms, and stochastic calculus applied to financial markets.
• Analytical Mindset: A sharp, detail-oriented approach to problem-solving, with the ability to articulate complex quantitative concepts to non-technical stakeholders.

Nice-to-Have

• Prior experience working specifically within a global macro investment mandate.
• Familiarity with cloud computing environments (AWS or GCP) and handling massive, unstructured data pipelines.
• Knowledge of global regulatory frameworks impacting physical and derivative commodity trading.