Model Validation- Quantitative Analyst Central Clearing

I am working with a prominent financial market infrastructure provider to appoint a Quantitative Analyst within its independent risk function. This is a hybrid role based in London, with three days per week required in the office

I am working with a prominent financial market infrastructure provider to appoint a Quantitative Analyst within its independent risk function. This is a hybrid role based in London, with three days per week required in the office. The position sits within the second line of defence and is focused on the validation of models used to measure financial risk.

The successful candidate will play a central role in assessing and challenging a wide range of risk models that support central clearing across commodities and derivatives markets. Direct experience within a central counterparty (CCP) environment is an important requiremen.

The role involves testing and validating models that quantify credit, market, and liquidity risk exposures. It requires hands-on technical capability in Python and SQL, alongside strong domain knowledge of CCP risk frameworks. You will collaborate with both first and second line teams, and your findings will feed directly into governance processes, including model risk committees and reporting to senior management. Although you'll be working in a technical capacity, a key part of your remit will also include writing clear validation reports, translating model behaviour into accessible insights, and ensuring model use remains aligned with regulatory and policy expectations.

I am looking for candidates with advanced academic backgrounds in quantitative disciplines such as mathematics, physics, statistics, or engineering, combined with practical experience in risk model validation or quantitative risk management. Ideally, this will have been gained within a CCP or with a financial institution directly supporting central clearing activity.

If you meet the above set criteria, please apply or send a copy of your CV to

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