Model Validation Vice President - Specialist, Risk Management Group

• Independent Model Validation: Conduct end-to-end independent validation of credit risk models, including but not limited to:
    o Wholesale Banking Models: PD, LGD, EAD models for corporate, commercial real estate, and other institutional exposures; stress testing models; counterparty credit risk models; credit rating models
    o Retail Banking Models: PD, LGD, EAD models for mortgages, auto loans, credit cards, personal loans; application scorecards, behavioral scorecards, collections models.

• Methodology Review: Critically evaluate model development methodologies, underlying assumptions, conceptual soundness, and quantitative techniques.

• Data Quality Assessment: Assess the quality, completeness, and appropriateness of data used in model development and ongoing monitoring.

• Implementation Review: Verify the accurate and robust implementation of models within relevant systems.

• Performance Monitoring: Review and challenge ongoing model performance monitoring frameworks and results, including backtesting, benchmarking, and sensitivity analysis.

• Governance and Policy: Establish Governance processes and Model validation Policy in line with local regulatory and group requirements.

• Documentation & Reporting: Prepare comprehensive validation reports detailing findings, limitations, recommendations, and conclusions. Present findings to model owners, developers, and relevant committees.

• Regulatory Compliance: Ensure models and validation practices comply with internal policies and external regulatory requirements (e.g., SR 11-7, CECL, IFRS 9, Basel Accords).

• Challenging Model Development: Proactively engage with model developers to provide constructive challenge and guidance throughout the model lifecycle.

• Stakeholder Engagement: Collaborate effectively with model development teams, risk management functions, business lines, internal audit, and regulators.

• Continuous Improvement: Contribute to the enhancement of validation processes, methodologies, and tools.

Required Experience
• 10+ years of experience in model validation or model development, specifically with exposure to credit risk models.
• Demonstrable experience with model review and validation pertaining to both wholesale banking and retail banking
•Understanding of technical modelling requirements throughout model life cycle covering data analytics, coding, development, deployment, monitoring and maintenance for both traditional AI and Gen AI models
• Capacity to adopt new modelling areas (e.g., climate risk & Agentic AI) and problem solving
• Understanding of model governance requirements and principles  

Education / Preferred Qualifications
Post -graduate/ Graduate degree in quantitative discipline (such as Statistics, Mathematics, Quantitative Finance, Data Analytics or equivalent) is preferred

Core Competencies             
- Deep understanding of credit risk concepts, including PD, LGD, EAD, ECL, credit ratings, credit scores, and stress testing. 
- Familiarity with relevant RBI and Basel guidelines
- Understanding of financial products and business processes in both wholesale and retail banking segments. 
- Analytical & Critical Thinking: Exceptional analytical, problem-solving, and critical thinking skills with the ability to identify subtle model weaknesses and propose practical solutions.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts clearly and concisely to both technical and non-technical audiences.
- Attention to Detail: Meticulous attention to detail and a commitment to producing high-quality work.
- Collaboration: Ability to work independently and as part of a team in a fast-paced environment.
- Experience in a regulated financial institution environment.

Technical Competencies           
- Strong proficiency in statistical modeling techniques: regression analysis, econometrics, time series analysis, machine learning algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting).
- Expertise in programming languages for data analysis and statistical modeling: Python, R, SAS. 
- Familiarity with database querying tools (SQL) is a plus.          
- Experience with large datasets and data manipulation.