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Quantitative Analyst – Commodities Derivatives Modelling
Location: London
Employment Type: Permanent
A leading global investment bank is seeking a highly skilled Quantitative Analyst to join its front-office quantitative analytics team, with a focus on commodities derivatives modelling. This role offers the opportunity to contribute to the design, development, and enhancement of state-of-the-art pricing models and volatility frameworks that underpin the firm’s global commodities trading and risk management activities.
The successful candidate will combine strong quantitative expertise with advanced programming capabilities and a deep understanding of derivative pricing and volatility modelling. While direct experience in commodities is preferred, relevant expertise in other asset classes such as FX or equities with complex volatility dynamics will also be considered.
Key Responsibilities:
- Develop, implement, and maintain pricing and risk models across a wide range of commodities derivatives.
- Design and calibrate volatility models and surfaces to support trading and risk functions.
- Deliver scalable and efficient analytics infrastructure using C++ and Python, aligned with front-office requirements.
- Work closely with trading, structuring, and risk teams to provide high-performance model-driven tools and analytics.
- Contribute to the ongoing enhancement and modernization of the quantitative library and risk analytics platform.
- Ensure full documentation of models in accordance with internal governance and regulatory expectations.
- Engage with model validation and risk control teams throughout the model approval and lifecycle management process.
- Provide analytical support and ensure robustness of models in production environments.
Required Qualifications & Experience:
- Advanced degree (MSc/PhD) in a quantitative discipline such as Mathematics, Physics, Financial Engineering, or Computer Science.
- Demonstrated experience in a front-office quantitative analytics or quant development role within a financial institution.
- Strong understanding of derivative pricing theory and volatility modelling techniques.
- Proficiency in C++ and Python for model development and numerical computation.
- Familiarity with structured software development practices, including version control, testing, and continuous integration.
- Excellent problem-solving skills, a high level of attention to detail, and strong written and verbal communication abilities.
- Experience preparing model documentation and engaging with model validation or governance functions.
Preferred Skills:
- Prior experience working with commodities markets (e.g. energy, metals, agriculture, power, or gas).
- Knowledge of advanced numerical techniques such as Monte Carlo simulation, PDE solvers, and volatility surface calibration.
