Quantitative Risk Analyst
Temps plein
Engie
Titre: Quantitative Analyst Pricing & Risk Model Quantitative Risk Analyst - Risk Methodologies & Pricing Models Location: Bruxelles - Belgique About ENGIE and Supply & Energy Management: ENGIE, a global leader in low-carbon energy and services, relies on its Global Business Unit Supply & Energy Management (GBU S&EM) to provide reliable, sustainable, and affordable energy to all its customers. This strategic unit optimizes the Group's and clients' production assets and designs tailored energy solutions for our 200,000 professional clients and 15 million consumers. The Global Business Unit Supply & Energy Management leverages ENGIE's assets to deliver secure and sustainable energy to its B2B and B2C customers. It uses its expertise in energy management to provide decarbonized electricity 24/7. Joining Supply & Energy Management means becoming part of a team of over 10,000 passionate experts spread across 20 countries, all united by a shared mission: shaping a greener and more efficient energy future. Together, we push the boundaries of innovation to deliver decarbonized energy 24/7. Join us and be part of those shaping the energy of tomorrow! Context As a Quantitative Risk Analyst, you will be part of a team that sits at the intersection of financial modeling, risk management, and software development. Your responsibilities will include validating pricing models for derivatives and designing and continuously improving risk models for market, credit, and liquidity risks. Your daily tasks will encompass three key areas:
- Advanced Financial Modeling : You will cultivate a critical mindset and challenge stochastic models through close interaction with traders and pricing model designers.
- Technical Development : You will implement and test models and their alternatives within a robust C# and Python codebase, integrated into a Big Data environment (AWS, Dataiku).
- Cross-Functional Collaboration : You will work closely with Risk, IT, and Front Office teams to transform complex problems into practical solutions, whether through ad hoc pricing tools, back-testing prototypes, or automated model monitoring systems.
- Perform model validation of pricing models for complex derivatives (e.g., exotic basket options, swing options, storage, renewables, ...).
- Implement validated stochastic models in C# within the risk methodologies pricing library.
- Build and test alternative models to benchmark and assess the robustness of valuation methods.
- Participate in internal model committees by providing theoretical and numerical justifications.
- Design risk metrics for market risks (VaR, SVaR, quantitative stress tests), implement prototypes and present method specifications for future IS integration.
- Design risk metrics for credit risks (PFE, CVA, DVA), implement prototypes and present method specifications for future IS integration.
- Ensure full documentation of models and methods for traceability, auditability, and regulatory compliance.
- Solid knowledge of stochastic models applied to derivative pricing and risk modeling.
- Strong background in statistics: hypothesis testing, time series analysis, regression models.
- Proficient in object-oriented programming (C#, Python), with the ability to independently develop models and prototypes.
- Familiarity with Big Data technologies, particularly Amazon Web Services (AWS) or Dataiku, is a plus.
- Previous experience in Commodity Markets is a strong advantage.
- Strong analytical skills, conceptual thinking, and scientific rigor.
- Ability to communicate complex concepts clearly and effectively to both technical and non-technical stakeholders.
- Team spirit and ability to work collaboratively in a fast-paced and multidisciplinary environment.
- Fluent in English
L'offre d'emploi a été publiée il y a 2 jours
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