Deputy Group Manager-R&C

Introduction

The Model Validation Analyst will independently evaluate the conceptual soundness, performance, implementation quality, and ongoing monitoring of models used throughout the Bank. The position strengthens the Bank’s Model Risk Management (MRM) framework by performing thorough model validations aligned with regulatory requirements, including the U.S. Federal Reserve’s SR 26-2 Guidance on Model Risk Management and applicable OCC and FDIC expectations.

The analyst will assess a broad mix of quantitative and qualitative model types, such as credit risk, market risk, liquidity risk, stress testing, AML/fraud, capital planning, CECL, pricing, and machine learning models. The role operates under the supervision of the U.S. team and requires close collaboration with the Bank’s MRM team, model developers, model owners, business stakeholders, Internal Audit, and regulators to ensure models are appropriate for their intended use and that related risks are identified and mitigated effectively.

Your Role And Responsibilities

• Conduct independent validations and reviews of models/EUCs across the model lifecycle, under the supervision of the U.S. team.
• Evaluate model conceptual design, methodology, assumptions, limitations, data quality, implementation, and usage.
• Execute quantitative testing, including:
• Outcome analysis
• Benchmarking
• Back-testing
• Sensitivity analysis
• Stress testing
• Performance monitoring reviews
• Review model development documentation and supporting evidence.
• Confirm adherence to internal model risk policies and regulatory guidance.
• Document validation findings and assign risk ratings according to established governance standards.
• Create recommendations to enhance model performance and support risk mitigation.
• Help maintain the model inventory, model risk ratings, and issue tracking and remediation.
• Work with the bank MRM team, model developers, business units, risk management teams, and technology teams.
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

Education

• Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, or related quantitative discipline
  
  

Experience

• 2-5 years of experience in model validation, model development, quantitative analytics, data science, risk management, or related fields within banking or financial services, preferably for U.S. clients.
• Knowledge of model risk management frameworks and regulatory expectations.
• Experience validating or developing risk, financial, or machine learning models is preferred.
  
  

Technical Skills

• Demonstrated command of statistical and quantitative modeling techniques.
• Familiarity with:
• Regression analysis
• Time series forecasting
• Classification models
• Machine learning methodologies
• Probability and statistical inference
• Proficiency in one or more programming languages:
• Python
• R
• SAS
• SQL
• Experience working with large datasets and analytical tools.
  

Preferred Technical And Professional Experience

Master's degree in a quantitative field.

Experience validating:

• Credit risk models
• CECL models
• Stress testing models
• Market risk models
• AML/Fraud models
• Machine learning and AI models
• Experience or familiarity with:
• SR 26-2 Model Risk Management Guidance
• OCC Model Risk Management expectations
• CECL framework
• Basel regulations
• Fair Lending and model governance requirements
• AI/ML model risk considerations
  
  

Competencies

• Strong analytical and critical thinking abilities.
• Capability to independently challenge complex quantitative approaches.
• Excellent written and verbal communication skills.
• Strong report-writing and documentation skills.
• High attention to detail and dedication to quality.
• Ability to manage multiple projects and deadlines.
• Strong skills in stakeholder engagement and relationship management.
  
  

Key Deliverables

• Thorough model validation reports.
• Validation test workpapers and supporting documentation.
• Findings and remediation tracking.
• Model performance monitoring assessments.