Model Risk Management Lead

• Provide oversight of the Bank’s Model Risk Management Program in alignment with the Bank’s risk appetite and supervisory guidance SR 26-2
• Oversee independent validation of all models, including non-models and end user computing (EUC)
• Ensure models are conceptually sound, statistically robust, fit for purpose, and validation activities are adequately documented
• Maintain a complete and accurate model inventory and development and validation documentation
• Review ongoing model performance monitoring and escalated deteriorating performance
• Monitor model use against approved use and use plans
• Track and report model issues and review issue remediation
• Assist in reporting consolidated model risk across the Bank
• Collaborate with model owners, developers, business stakeholders, and department heads
• Communicate model risk management policies, procedures, and expectations
• Monitor industry trends, regulatory changes, and emerging model risk
• Assist with regulatory examinations and internal and external audits; respond to findings and corrective actions

• Bachelor’s degree in a highly quantitative discipline (e.g. Statistics, Mathematics, Engineering, or Data Science)
• Minimum 5 years of experience in Model Development or Model Validation
• Proficiency in statistical programming languages (e.g., SAS, Python, R, or SQL)
• Extensive knowledge of regression techniques, machine learning, time series techniques, and other statistical models
• Ability to explain complex mathematical concepts to non-technical stakeholders and senior management
• Ability to work autonomously with strong ownership mindset
• Excellent organizational skills and ability to work well under pressure independently and collaboratively
• Must be legally authorized to work in the United States
• Visa sponsorship is not available

Demonstrates expertise in Model Risk Management, including oversight of model validation and development, statistical programming, and regulatory compliance. Capable of effectively communicating complex concepts to diverse stakeholders while maintaining a robust model inventory and performance monitoring.

• Model Risk Management
• Model Validation
• Statistical Programming (SAS, Python, R, SQL)
• Regression Techniques
• Machine Learning

• Model Development
• Statistical Programming
• Regression Techniques
• Machine Learning
• Time Series Techniques
• Model Performance Monitoring
• Model Inventory Management
• Documentation
• Data Science
• Quantitative Analysis

• Communication
• Organizational Skills
• Autonomy
• Collaboration
• Pressure Management

• Regulatory Compliance
• Model Risk
• EUC
• Supervisory Guidance SR 26-2
• Internal Audits
• External Audits
• Model Issues
• Issue Remediation
• Risk Appetite
• Industry Trends