Model Validation Sr. Lead

JOB PURPOSE / ROLE:

The Model Validation Sr. Lead is responsible for independently validating all quantitative and qualitative models used within the Bank. They directs a team to assess models integrity, ensure compliance with internal and regulatory Model Validation Standards and thus enforce robust risk management practices.

Key Responsibilities:

• Model Validation Policy/Framework/Standards & Quality Assurance:

Framework/Policy Development & Maintenance: Design, refine/maintain, and enforce the Bank's Model Validation Policy, Standards, and procedures to ensure adherence to internal appetites and external regulatory requirements.

Independent model oversight: Lead the end-to-end model validation framework, setting enterprise direction to ensure all quantitative/qualitative models operate as intended.

Regulatory & Internal Liaisons: Serve as the primary point of contact for internal audits and external regulatory exams regarding model validation.

Own the Model Validation standards, guidelines, procedures and templates

Establish and oversee quality assurance mechanisms – including peer review, thematic reviews and consistency checks – to ensure embedment of high-quality validation practices.

• Leadership & Team Management:

Leads a high-performing, multidisciplinary model validation team responsible for validating a diverse portfolio of models including risk management, investment, fraud, compliance, finance and HR, as well as Gen AI and Agentic use cases (among others).

Develop and mentor talent to promote strong technical capabilities and a high-quality validation process.

• Model Validation Oversight, Reporting & Approval:

Ensure validation conclusions are robust, well-supported and communicated clearly to stakeholders with varying levels of technical expertise.

Deliver clear and actionable model validation reports (focusing on key model risks, model uncertainty, issue remediation and emerging trends).

Being the Secretary of the Bank's Risk Analytics Working Group, to ensure the submission of model validation reports to the appropriate Risk Management Committee for approval.

• Methodology & Practice Leadership:

Define, maintain and continually enhance the methodologies and test approaches used in Model Validation

Ensure comprehensive assessment of conceptual soundness, model developmental evidence, performance, data approaches, implementation accuracy and other model validation criteria.

Lead the evolution of validation techniques for emerging modeling approaches including LLM-enabled and agentic systems

• Relationship Management:

Serve as a key point of contact for regulators, internal audit and senior leaders on Model Validation related matters.

Articulate validation rationales, modeling assumptions and risk implications clearly and confidently to supervisory authorities and executive stakeholders.

Technical Knowledge areas:

• Extensive experience with a broad range of model types, including Machine Learning/LLM-based models
• Strong understanding of model risk management principles and regulatory frameworks (e.g., SR26-2, SS1/23) and demonstrates experience engaging with regulators and internal audit
• Strong Technical Proficiency with programming languages and analytical tools such as Python, SAS, R or C++ and familiarity with emerging technologies, AI governance and modern model validation or development practices.
• Proven ability to translate complex technical concepts into clear, actionable insights for Senior Executives.
• Excellent analytical and problem-solving skill, with the ability to work independently through complex challenges and tight timelines
• Proactive, self-directed, and able to work independently and collaboratively within a team.
• Strong written and verbal communication skills, including experience presenting to Senior Committees, Executives and regulatory bodies.
• Strong organizational skills and the ability to manage efficiently multiple priorities under tight deadlines.

QUALIFICATIONS & EXPERIENCE:

Minimum Qualifications:

• Advanced degree (Master's or Ph.D.) in a quantitative field such as Mathematics, Statistics, Finance, Data Science, Financial Engineering, Computer Science, or Economics.

Minimum Experience:

• 8 - 10 years of relevant experience across model validation, model risk management and model development, including a minimum of 5 years leading multi-layered model validation teams.

Language:

English: Advanced