Vice President, Model Validation

Overview

In this role you will perform independent validation of quantitative models within MUFG’s EMEA Model Risk Management team in London. You will design challenger models, review frameworks and assumptions, test numerical implementations, and prepare validation reports for oversight committees. You’ll collaborate with risk analytics and front office quants to ensure regulatory-compliant, high-quality models across asset classes with a focus on PFE and XVA. This is a growth-focused opportunity to strengthen MUFG’s risk modelling capabilities in a globally respected bank.

Pay / Benefits

• flexible working requests
• training opportunities
• inclusive culture
• professional growth
• collaborative environment

Responsibilities

• Conduct initial and periodic validation of quantitative models across asset classes, ensuring regulatory compliance and best practices.
• Design, prototype and test challenger models to provide alternative perspectives on methodologies.
• Perform thorough quantitative analysis of model frameworks, inputs, and results for accuracy and reliability.
• Review documentation and test numerical implementations; ensure governance standards are met.
• Document findings and raise recommendations to improve model performance and mitigate risks.
• Track remediation actions and stay updated on industry advances to drive continuous modelling enhancements.
• Prepare model risk reports for Model Oversight Committees and the Board to support decision-making.
• Collaborate with Risk Analytics and Front Office quants to facilitate knowledge sharing and model improvements.
• Stay informed on latest developments in financial mathematics and risk modelling techniques.

Key requirements

• Experience in quantitative modelling (development or validation) in counterparty credit risk or derivatives pricing.
• Experience developing or validating xVA models.
• Postgraduate degree in a quantitative discipline.
• Strong mathematics and probability theory background applied to finance.
• Proficiency in Python or R; ability to implement complex models.
• Knowledge of simulation techniques and numerical methods in finance.
• Understanding of data science concepts and statistical inference for risk.
• Excellent written and oral communication skills; detail-oriented and methodical.

• clear communication
• collaborative mindset
• attention to detail
• Python
• R
• simulation techniques

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