Euronext Clearing- Senior Associate, Financial Risk LOD2

Overview

In this Senior Quantitative Risk Analyst role, you'll provide independent challenge to CCP risk models within the ROC function of the LOD2. You will assess margin, default fund, and risk frameworks across multiple asset classes, ensuring robustness under stressed conditions. The role blends quantitative modelling, governance, and regulatory expectations, with a focus on challenging rather than building models. You'll work closely with stakeholders to strengthen controls and testing methodologies, contributing to a credible risk framework. This position offers high visibility and impact in EMIR-driven regulatory contexts and CCP risk oversight.

Responsabilità

• Independent challenge of Initial Margin, Default Fund and stress testing frameworks across asset classes
• Analyse model assumptions and behavior under stressed and non-linear conditions
• Identify weaknesses and propose recalibration or redesign
• Contribute to reviews of new models, parameter changes, and new business initiatives
• Execute and enhance EMIR-mandated tests (Backtesting, Sensitivity Analysis, Reverse Stress Testing)
• Analyse results for performance, stability, and procyclicality
• Support interpretation and escalation of key findings
• Design and perform quantitative controls on margin, default fund, and key risk metrics
• Develop anomaly detection approaches and investigate outliers
• Contribute to regulatory monitoring and internal reporting
• Contribute to tools, dashboards, and automation initiatives

Requisiti fondamentali

• Degree in Mathematics, Physics, Engineering, Quantitative Finance or similar
• Strong understanding of financial risk concepts
• Analytical mindset with ability to question models
• Good programming skills (Python preferred) for data analysis and modelling
• Ability to work independently on complex problems
• Strong analytical thinking
• Independent and proactive problem solver
• Effective communication with senior stakeholders
• Python programming
• Model validation and stress testing
• Risk metrics (VaR, Expected Shortfall)