Model Risk Governance Specialist Quantification

Hybrid

Job number

33044

Category

Senior Professional

Status: Permanent

Schedule: Full-Time

28-May-2026

Area(s) of interest: Risk management

A career as a Model Risk Governance Specialist - Risk Assessment in the Model Risk Management team at National Bank means acting as a quantitative governance specialist responsible for evolving, calibrating and maintaining the Bank’s established model risk assessment methodologies.
In this role, you help strengthen model risk governance by ensuring that model risk is assessed consistently across the organization, based on the inherent risk of using a model, including model vulnerabilities, materiality of impacts, model uncertainty, limitations, usage and ongoing monitoring results.

• Drive the ongoing evolution, calibration and expansion of the Bank’s established model risk rating and assessment methodology, including practices related to inherent model risk, model vulnerabilities, materiality of impacts, uncertainty, limitations and usage.
• Define and calibrate clear, measurable criteria to support consistent model risk ratings across model types, business lines and risk categories, using both quantitative and qualitative factors.
• Establish assessment dimensions that consider model complexity, methodology, assumptions, data reliability, model autonomy, intended use, business purpose, customer impact, regulatory exposure and potential financial, operational or reputational impacts.
• Apply quantitative judgment to evaluate key drivers of model risk, including model uncertainty, sensitivity to assumptions, data quality, performance stability, model limitations and changes in model use or environment.
• Review and challenge the application of the model risk rating approach by model stakeholders to ensure assessments are complete, well‑supported, consistently applied and aligned with governance expectations.
• Define expectations for assigning, reviewing and updating model risk ratings throughout the model lifecycle, including when trigger events occur such as material model changes, changes in use, performance deterioration, data changes or infrastructure changes.
• Analyze ongoing monitoring results, model limitations, validation outcomes, usage patterns and control information to determine whether the model risk rating approach remains appropriate and effective.
• Support visibility of both inherent and residual model risk for governance reporting, while ensuring that oversight expectations remain aligned with the level and nature of inherent model risk.
• Identify trends, inconsistencies, emerging themes and areas requiring methodology refinement, additional guidance or governance attention.
• Recommend enhancements to the assessment methodology, rating criteria, documentation standards, governance tools and reporting practices.
• Prepare decision‑ready materials for senior management and governance committees, highlighting model risk rating outcomes, methodology considerations, material limitations, impact analysis, emerging themes and items requiring attention or decision.
• Collaborate with model owners, model developers, validators, business lines, technology teams and risk partners to ensure the model risk rating methodology is understood, applied consistently and integrated into governance processes.
• Provide advisory support and training to stakeholders on model risk rating expectations, including inherent risk, materiality, uncertainty, limitations, monitoring results, trigger events and governance implications.

• Hold a Bachelor’s degree in mathematics, statistics, economics, finance, engineering, computer science, data science, actuarial science, risk management or a related quantitative field and 7 years of relevant experience; OR a Master’s degree in a quantitative, finance, technology or risk‑related field and 5 years of relevant experience.
• Experience in model risk management, model validation, quantitative risk assessment, risk methodology, risk analytics, statistical modelling, financial modelling or data science in financial services.
• Strong understanding of model risk concepts, including inherent model risk, model vulnerabilities, materiality, uncertainty,…