Head of Validation, Model Risk Management in City of Westminster

The Head of Validation, Model Risk is a senior leadership role responsible for setting enterprise direction for model validation, delivering independent, risk-based oversight across a diverse portfolio of models spanning investment and risk management, fraud and compliance, finance and HR, and rapidly evolving Gen AI and agentic use cases. This role serves as the senior authority for model validation, setting the bar for defensible methodologies, rigorous challenge, and clear, decision-ready risk communication to senior leaders. It strengthens model risk culture and lifecycle discipline across the enterprise, driving timely issue remediation, elevating validation quality and consistency, and ensuring Vanguard's practices remain aligned with regulatory and audit expectations.

Leadership & Team Management

• Leads a high-performing, multidisciplinary model validation team responsible for validating a diverse portfolio of models including investment and risk management, fraud and compliance, finance and HR, as well as Gen AI and Agentic use cases.
• Develops and mentors talent to promote strong technical capabilities and a high-quality validation process.

Validation Oversight & Approval

• Serves as the final approval authority for validation reports on higher-risk models.
• Ensures validation conclusions are robust, well-supported, and communicated clearly to stakeholders with varying levels of technical expertise.

Model Risk Governance & Lifecycle Management

• Oversees adherence to enterprise model lifecycle requirements—including model inventory accuracy, change management, ongoing monitoring, and issue remediation.
• Drives timely resolution of model-related issues and non-compliance, escalating when necessary.
• Strengthens model-risk culture across the enterprise through targeted training, outreach, and proactive engagement with model owners and developers.

Methodology & Practice Leadership

• Defines, maintains, and continually enhances the methodologies and test approaches used in model validation.
• Ensures comprehensive assessment of conceptual soundness, performance, data quality, implementation accuracy, and other model risk considerations.
• Leads the evolution of validation techniques for emerging modeling approaches, including LLM-enabled and agentic systems.

Standards, Policies & Quality Assurance

• Owns the enterprise’s model development and model validation standards, guidelines, procedures, and templates.
• Establishes and oversees quality assurance mechanisms—including peer review, thematic reviews, and consistency checks—to ensure embedding of high-quality validation practices.

Executive Reporting & Model Risk Insights

• Delivers clear, actionable reporting on key model risks, model uncertainty, issue remediation, and emerging trends to senior committees and executives.
• Supports the development and enhancement of divisional and enterprise model-quality scorecards and contributes to the risk-appetite process.

Senior Stakeholder, Regulatory & Audit Engagement

• Serves as a primary point of contact for regulators, internal audit, and senior leaders on model validation related matters.
• Articulates validation rationales, modeling assumptions, and risk implications clearly and confidently to supervisory authorities and executive stakeholders.

Qualifications

• Advanced degree in a technical field (e.g., Master's or doctoral degree in a quantitative discipline such as Mathematics, Statistics, or Economics).
• 10+ years of experience across model development, model validation, and model risk management, including a minimum of five years leading multi-layered model validation teams.
• Extensive experience with a broad range of model types, including machine learning/LLM-based models.
• Deep knowledge of model-risk management principles and regulatory frameworks (e.g., SR26-2, SS1/23) and demonstrated experience engaging with regulators and internal audit.
• Strong technical proficiency with programming languages and analytical tools such as Python, R, or C++, and familiarity with emerging technologies, AI governance, and modern model development practices.
• Proven ability to translate complex technical concepts into clear, actionable insights for senior executives.
• Exceptional written and verbal communication skills, including experience presenting to senior committees, executives, and regulatory bodies.
• Demonstrated ability to partner with stakeholders to balance effective challenge, practical solutions, and business objectives.