Quantitative Modeller, Commodities
About the Role
We are seeking a highly analytical and commercially minded Commodities Modelling Quant Analyst to join a London‑based quantitative analytics team within a leading global energy company. This role sits at the intersection of financial engineering, data science, and energy market fundamentals. You will contribute directly to the development of cutting‑edge modelling tools that support trading, risk management, and commercial strategy across power, gas, LNG, and environmental products.
As a key member of the front‑office quant group, you will design, implement, and maintain quantitative models used for pricing, valuation, optimisation, and scenario analysis. You will work closely with traders, structurers, data engineers, and risk teams to translate complex market dynamics into robust analytical frameworks that drive commercial decision‑making. This is an excellent opportunity for someone who thrives in a dynamic environment, enjoys solving complex problems, and wants to shape the future of global energy markets.
Key Responsibilities
- Develop and maintain stochastic models, fundamental market simulations, and derivatives pricing frameworks for energy and commodity markets.
- Build forecasting, optimisation, and machine‑learning models to improve trading signals and asset‑backed strategies.
- Collaborate with trading desks to enhance valuation methodologies for structured products, storage, transportation, and long‑term supply contracts.
- Design and implement robust Python‑based analytical libraries, ensuring high‑quality, well‑tested, and maintainable code.
- Analyse large, complex datasets related to market fundamentals, weather patterns, asset performance, and macroeconomic indicators.
- Support daily commercial activities by providing timely quantitative insights, pricing analysis, and model improvements.
- Work with quantitative risk teams to validate models, address model risk, and ensure compliance with internal governance standards.
- Stay current with market developments, modelling techniques, and technological innovations relevant to global energy markets.
Qualifications & Experience
- Advanced degree (MSc/PhD) in Mathematics, Physics, Engineering, Computer Science, or another quantitative discipline.
- Strong proficiency in Python (NumPy, Pandas, SciPy), with experience in building production‑grade analytics tools.
- Solid understanding of energy markets, derivatives pricing, time‑series modelling, and optimisation techniques.
- Experience with stochastic calculus, Monte‑Carlo simulation, and numerical methods such as PDE solvers is highly desirable.
- Familiarity with machine learning frameworks (e.g., scikit‑learn, TensorFlow, PyTorch) is an advantage.
- Strong communication skills, with the ability to explain complex concepts to non‑technical stakeholders.
- Ability to work in a fast‑paced environment with shifting priorities and high commercial impact.
What We Offer
- Opportunity to work at a leading global energy company driving innovation in commodity analytics and decarbonised energy markets.
- Exposure to front‑office trading, structured transactions, and large‑scale optimisation challenges.
- Competitive compensation, bonus potential, and benefits.
- A collaborative, intellectually stimulating environment with significant professional growth opportunities.
FAQs
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