Quantitative Risk Analyst

Job Description

Work in a highly experienced, collaborative, international, fun team that sits at the heart of E.ON's new energy trading and procurement unit; Help shape the quantitative risk management function for a fast-growing trading portfolio spanning power, gas, options, complex financial and physical derivatives, and structured customer business; Take end-to-end ownership of models: from idea and research, through prototype, to robust, cloud-native production implementation; Develop, maintain, and enhance quantitative risk and pricing models for market, credit, and liquidity risk, including simulation and valuation tools for complex energy portfolios; Design and run Monte Carlo and scenario simulations (e.g. for P&L, exposure, stress and what-if analyses) to support decision-making on transaction, portfolio, and business level; Implement models and analytics in Python as part of a modern cloud-native stack (e.g. Azure, containerized services, Snowflake-based data platform), following clean code and DevOps practices (Git, CI/CD, testing); Use and further develop data-driven and ML-inspired approaches (e.g. time series models, tree-based models, regularization, feature engineering) where they add value to risk management and pricing; Closely collaborate with Trading, Market and Credit Risk, Finance and Tech in agile projects to solve real business problems and deliver tangible value; Translate complex quantitative concepts into clear, actionable messages for senior, non-technical, and executive stakeholders and continuously explore new methods and research in risk modelling, quantitative finance and data science - and bring the most useful ideas into our production landscape; Mentor juniors, working students and interns, helping them grow their quantitative, coding and business skills.

What you bring

• A Master's or PhD in a quantitative field (Mathematics, Physics, Statistics, Financial Mathematics, Computer Science, Engineering or similar);
• 3+ years of experience in quantitative risk modelling, valuation, or pricing - energy trading experience is a strong plus, but we're also open to candidates from other asset classes with genuine interest in power & gas;
• Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks;
• Solid understanding of stochastic processes, Monte Carlo simulation, and time series analysis; familiarity with derivative pricing, valuation and risk metrics (e.g. VaR, ES, sensitivities) is highly welcome;
• Practical exposure to data-driven/ML techniques for forecasting, scenario generation or model calibration is a plus;
• Experience working with databases and cloud/data platforms (SQL, Snowflake, Azure, or similar) is a plus;
• Excellent analytical and problem-solving skills, with a pragmatic mindset and strong attention to detail;
• A self-starter, hands-on mindset: you enjoy taking ownership, working in cross-functional teams and driving topics from idea to implementation in a dynamic environment;
• Willingness to work collaboratively: Pair coding, Code reviews;
• Very good communication skills in English (German and Italian are a plus) and the ability to explain complex topics to non-quants.