Senior Model Risk Management & Validation Specialist - Financial Models, AI/ML, AML, Credit Risk, and Regulatory Compliance

Model Risk Management requirements:

Responsible for managing the execution of validation activities for financial models, acquisition and portfolio management scorecards, AML models and anti-fraud tools in accordance with the governance framework, policies and procedures, and provide recommendations for continuous model enhancements across the enterprise. Actively manages and escalates risk and customer-impacting issues within the day-to-day role to management.

• Manages independent model validations and effective challenge of financial and account management models such as balance sheet/ALM, revenue, expense, credit loss, economic capital, regulatory capital, treasury, interest rate, acquisition scorecards, ML/AI models, AML models, and operational risk models.
• Interprets model validation test results and establish required action plans with model owners/developers and provide value-added recommendations to model owners/developers.
• Maintains current/develop new analytical reports and presentations for senior management, executive committees and regulatory exams.
• Actively participates in the production, maintenance and compliance with model validation policies, standards and procedures.
• Proactively identifies emerging model risk issues impacting the Company and communicates to model developers, senior management and the DFS Model Governance Committee.
• Maintains standardized model validation documentation, and keeps up to date with regulations, regulatory exam requirements and regulatory guidance.
• Interacts with external regulators and internal auditors to demonstrate the operational soundness and effectiveness of the model validation process.

Financial Model Validation:

Financial Model Validation Specialists will be responsible for evaluating and assess the accuracy, reliability, and suitability of financial models used by organizations. They ensure models meet regulatory requirements, internal standards, and business objectives, ultimately mitigating model risk. This involves a multi-faceted process including conceptual soundness, outcome analysis, and ongoing monitoring.

• Hands-on validation or development experience (2+ years) in loss forecasting models, PPNR models, Economic Capital models, or market and liquidity risk models
• Strong skills in statistical models, time series analysis, or econometrics
• Independently evaluate models, including statistical, financial, and machine learning models, across various business areas.
• Strong skills in Python and Excel
• Strong programming skills in Python, R, and SQL
• Outstanding knowledge in statistics, mathematics, econometrics, or finance
• Model Risk validation folks with hands on experience statistical modeling or model validation experience.
• Tools knowledge to Validate Models
• Independent testing and Challenge of Model Design
• Question Data performance and assumption Before and After Model deployment
• Cut across different Lines of Business, a good understanding on Banking , Fair Lending Policies , Credit Card, Finance etc is a significant Plus.

Examples of Models validated can include

Examples of Models Validated:

• Credit Risk Models: Including but not limited to IFRS9, scoring models, IRB models, stress testing, and ICAAP.
• Market Risk Models: Assessing and managing market risks.
• ALM Models: Evaluating asset and liability management strategies.
• Mortgage Servicing Rights (MSR) Valuation Models: Assessing the value of mortgage servicing rights.
• CECL Models: Validating Current Expected Credit Loss models.
• AML Models: Assessing and validating anti-money laundering models.