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• To strengthen our Quantitative & Model Risk team, we are looking for a Quantitative Risk Manager to advance the mathematical modelling of our key risk indicators and ensure that non‑linear effects and non‑price‑related risks are properly considered. Reporting to the Head of Quantitative & Model Risk, you will develop and implement key risk indicators used for market risk management, set up simulations for the future evolution of relevant risk factors, and contribute to model risk management by validating valuation models built by the Front Office
• Developing and advancing key risk indicators that reflect all relevant risk factors for energy-related transactions, including price, volume and non‑linear effects
• Implementing mathematical and statistical models for the purpose of measuring non-linear risks via full revaluation
• Performing model validation for valuation models developed by the Front Office
• Designing and maintaining consistent, correlated simulations of relevant risk factors (e.g. price and weather variables)
• Integration of the developed key risk indicators and models into the Alpiq risk infrastructure from a conceptual and technical perspective together with the team

Your profile

• MSc., ideally complemented by a Ph.D., in Mathematical Finance, Mathematics, or a related quantitative field
• At least 2 years of relevant experience in building valuation models, risk models or similar quantitative frameworks
• Strong knowledge of stochastic calculus
• Experience in simulating price diffusions
• Practical understanding of derivative pricing
• Solid programming experience in Python, including Pandas and NumPy
• Knowledge of relational database design, SQL and Oracle
• Experience with GitLab or similar version control tools
• Knowledge of power and gas markets and products and understanding of core risk management concepts is a plus
• Experience with machine learning concepts (e.g. neural networks, reinforcement learning) is an advantage
• You appreciate working in a team of risk managers with different backgrounds