Principal, Model Risk Management

Principal, Model Risk Management (Hybrid - Columbia, SC)

The Principal, Model Risk Management serves as the Bank's primary subject matter expert and advisor for Model Risk Management and is responsible for the administration, enhancement, and independent oversight of the Bank's Model Risk Management Program. This role develops, maintains, and enhances the Bank's Model Risk Management framework, policies, standards, methodologies, and governance processes to ensure alignment with regulatory expectations and industry practices, provides enterprise-wide guidance on model governance, model inventory management, validation activities, model risk assessments, and regulatory compliance related to model risk.

As a senior individual contributor, this position partners with Finance, Treasury, Credit, Technology, Data, Artificial Intelligence, Operational Risk, Compliance, and business leaders to ensure models are appropriately identified, governed, validated, monitored, and maintained throughout their lifecycle. The role provides independent challenge regarding model assumptions, methodologies, limitations, controls, and performance while supporting management's accountability for model ownership and outcomes.

This position also coordinates independent model reviews performed by external validation providers, conducts independent validations of designated models, and provides objective assessments of model conceptual soundness, implementation, performance, and governance.

What You'll Do

Model Validation Oversight and Independent Challenge 

• Coordinate independent model validations and periodic reviews performed by external validation providers, and independently perform validations of designated models in accordance with the Bank’s Model Risk Management framework.
• Develop and maintain a risk-based model validation and review schedule based on model risk ratings, complexity, materiality, regulatory expectations, and model lifecycle requirements.
• Execute end-to-end validation activities, including assessments of model design, conceptual soundness, methodology, assumptions, data quality, implementation, performance monitoring, and outcome analysis.
• Develop validation work programs, testing methodologies, and supporting documentation to support independent conclusions regarding model effectiveness and appropriateness.
• Provide independent challenge regarding model methodologies, assumptions, limitations, controls, performance monitoring practices, and compensating controls.
• Document validation results, communicate findings and risk implications to management and model owners, monitor remediation activities, and escalate significant model risks or governance concerns as appropriate.
• Prepare and present model risk reporting, validation results, significant findings, remediation status, and emerging model risk themes to senior management, risk committees, and governance forums.

Model Governance and Lifecycle Oversight 

• Maintain oversight of the Bank’s model inventory and model risk classifications to ensure models are appropriately identified, risk-rated, and governed throughout their lifecycle.
• Monitor overall Model Risk Management Program effectiveness and identify opportunities to enhance governance, oversight processes, validation practices, model inventory administration, and regulatory compliance.
• Review significant model implementations, modifications, and retirements to ensure adherence to model governance requirements.
• Assess model risk associated with analytical tools supporting interest rate risk, liquidity risk, market risk, credit risk, stress testing, forecasting, capital planning, and other significant business activities.
• Evaluate model performance monitoring processes and the effectiveness of model controls.
• Monitor model risk metrics, aggregate model risk exposures, and adherence to approved model risk appetite and tolerance thresholds, escalating exceptions as appropriate.
• Provide technical guidance regarding model development standards, documentation, validation expectations, and ongoing monitoring practices.

Quantitative Risk Expertise and Emerging Model Risk Practices 

• Maintain expertise regarding quantitative methodologies and analytical practices used throughout the Bank.
• Evaluate model assumptions, quantitative methodologies, data sources, analytical techniques, and model outputs through objective analysis and testing.
• Monitor emerging quantitative methodologies, analytical tools, regulatory expectations, and industry practices relevant to model governance.
• Evaluate the implications of evolving technologies, including artificial intelligence and advanced analytics, on the Bank’s model risk profile and governance framework.
• Oversee model-related risks associated with artificial intelligence and advanced analytics that meet the Bank's definition of a model and coordinate with stakeholders regarding technology, cybersecurity, privacy, operational resilience, third-party, and other non-model risks associated with AI implementations.
• Advise senior management, governance committees, model owners, and other stakeholders on model risk considerations, validation expectations, emerging risks, regulatory developments, and required remediation activities.

What You'll Need

• Bachelor's degree in Finance, Economics, Mathematics, Statistics, Data Science, Risk Management, Business, or related field. Master's degree preferred.
• 10+ years of progressively responsible experience in model risk management, quantitative analytics, risk management, banking, financial services, treasury, finance, internal audit, regulatory supervision, or related discipline.
• 7-9 years of experience designing, administering, validating, or overseeing Model Risk Management and validation programs.
• 7-9 years of experience developing validation work programs, executing independent testing, documenting conclusions, and communicating validation results to senior management.
• 7-9 years of experience performing model validations, quantitative reviews, or independent analytical assessments of financial, forecasting, stress testing, credit, liquidity, market risk, operational, or vendor models.
• 4-6 years of experience interacting with regulators, auditors, executive leadership, governance committees, or external validation providers.
• 4-6 years of experience with artificial intelligence, machine learning, advanced analytics, or emerging model governance considerations.
• Expert knowledge of Model Risk Management principles, model governance frameworks, validation methodologies, model lifecycle management, and regulatory guidance, including SR 11-7 and related supervisory expectations.
• Ability to independently perform model validations and assess conceptual soundness, implementation accuracy, design effectiveness, performance monitoring, governance controls, and model outcomes.
• Ability to independently challenge quantitative methodologies, assumptions, data sources, analytical techniques, model limitations, controls, and outputs through objective analysis and testing.
• Strong knowledge of quantitative methodologies used in interest rate risk, liquidity risk, market risk, credit risk, stress testing, scenario analysis, capital planning, forecasting, and financial decision-support models.
• Strong analytical, critical-thinking, problem-solving, written communication, and stakeholder advisory skills, including the ability to communicate complex model risk concepts to management and governance stakeholders.