VP Quant (Model Validation / Cross Asset Classes / FI)

Our client is a leading financial institution. They are seeking a senior quantitative specialist to lead independent model validation across a broad and complex risk model universe.

The Role

Reporting to the Head of Model Validation, the incumbent will serve as the primary point of contact for all model validation activities. The role encompasses independent validation of margin, credit stress testing, derivatives pricing, collateral, liquidity stress, credit rating, and VaR models — ensuring these are accurate, robust, and fit for purpose.

You will work closely within the risk, data, analytics and quant teams to scope and prioritise validation work, deliver quarterly validation reports, track findings, and ensure timely resolution of issues. Beyond validation, the role carries cross-functional responsibilities including support for new product launches, regulatory compliance (PFMI principles and MAS requirements), and digitalisation of the validation function through analytics and AI tooling.

There is a clear mandate for leadership development, with the expectation of progression into a Team Lead role.

Requirements

• Degree in quantitative finance, data science, mathematics, engineering, or statistics; postgraduate qualification preferred
• At least 10 years of progressive experience in risk analytics, model development, or model validation
• Strong understanding of derivatives pricing models and market risk concepts including stress testing, VaR, mark-to-market, and risk sensitivities across asset classes
• Working knowledge of capital markets instruments — fixed income, equities, FX, and commodities; exposure to credit risk modelling is an advantage
• Strong Python skills with experience in JupyterLab or similar environments; familiarity with AI-assisted development tools, version control, large datasets, and SQL
• Familiarity with market data platforms such as Bloomberg and/or Reuters

Attributes

The ideal candidate is results-oriented, analytically rigorous, and a clear communicator who can translate complex quantitative concepts for non-technical audiences. You are curious, self-motivated, and genuinely excited about the role of AI in modern risk management.

Interested candidates are invited to send their CV to [email protected], quoting reference A07243.

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