Euronext Clearing- Senior Associate, Financial Risk
Pubblicato il 03-08-2026 - Euronext in Roma
Join us as a Senior Quantitative Risk Analyst – Financial Risk (LOD2, CCP)
Location: Rome (Hybrid)
Team: Clearing Risk – LOD2 Financial & Credit Risk
Reporting to: Head of Financial & Credit Risk
Gross Annual Salary: 45.000 - 50.000 euro
About the role
We are looking for a quantitative profile to join the Risk Oversight & Controls (ROC) function within the Second Line of Defence (LOD2) of a Central Counterparty (CCP).
The role is focused on independent model challenge, EMIR regulatory testing and controls across margin, default fund, and risk frameworks.
This is a high-impact position: you will not develop models, but you will assess, challenge, and strengthen them. You will work at the intersection of quantitative modelling, risk governance, and regulatory expectations.
Key responsibilities
Model Challenge (core focus)
- Perform independent challenge of Initial Margin, Default Fund, and stress testing frameworks across asset classes (Fixed Income, Equities, Derivatives, Commodities, Power)
- Analyse model assumptions, limitations,
and behaviour under stressed and non-linear market conditions
- Identify weaknesses and propose improvements, recalibration, or redesign
- Contribute to structured reviews of new models, parameter changes, and new business initiatives
EMIR Tests & Quantitative Validation Activities
- Execute and enhance EMIR-mandated tests (Backtesting, Sensitivity Analysis, Reverse Stress Testing)
- Analyse results with a critical view on model performance, stability, and procyclicality
- Support interpretation of outcomes and escalation of key findings
- Contribute to continuous improvement of testing frameworks and methodologies
Controls & Risk Monitoring
- Design and perform quantitative controls on margin, default fund, and key risk metrics
- Monitor consistency, stability, and risk sensitivity of model outputs
- Develop anomaly detection approaches (including data-driven or ML-based techniques)
- Investigate outliers and perform deep-dive analys