Model Risk Manager - Iselin, NJ

Provident Bank•Woodbridge Township, NJ

10d•$142,000 - $163,300•Onsite

About The Position

The Model Risk Manager serves as Provident Bank’s independent second-line authority for Model Risk Management (MRM), reporting to the Chief Risk Officer. The role provides effective challenge and oversight of the Bank’s models, quantitative methods, analytical tools, and model-driven decision-support systems. It maintains and enhances the Bank’s MRM Framework in alignment with regulatory expectations and industry practices. The position oversees the full model lifecycle, including identification, inventory management, risk tiering, validation, implementation oversight, performance monitoring, change management, and retirement. It performs and oversees independent validations, manages third-party validation providers, challenges model owners and business lines, and reports model risk exposures and emerging concerns to management and governance committees. The role also supports oversight of model-related data dependencies and risks associated with artificial intelligence, machine learning, advanced analytics, and evolving decision-support technologies. The role chairs the Model Risk Management Committee and coordinates with management, Audit, regulators, external auditors, consultants, and internal partner functions. Work is performed in a normal office environment with usually moderate noise levels.

Requirements

5–7 years of professional experience involving quantitative analysis, statistical modeling, model validation, model governance, or model risk management.
At least 3 years of experience in financial services, banking, consulting, regulatory, audit, or risk management environments involving quantitative model oversight.
Financial-industry experience in model risk management or model use, with sufficient quantitative and technical experience to perform validations.
Strong knowledge of SR 11-7 and OCC and FDIC supervisory expectations relating to model risk management.
Understanding of statistical modeling, quantitative analytics, machine learning concepts, forecasting methodologies, and risk measurement techniques.
Experience performing or overseeing independent model validations.
Familiarity with CECL, ALM, liquidity, capital planning, stress testing, fraud analytics, and other banking-focused quantitative models.
Ability to independently challenge model developers, business-line management, vendors, and third parties.
Knowledge of data governance, data lineage, data-quality management, and control frameworks supporting model risk management.
Excellent written and verbal communication skills, including the ability to present technical concepts to executive management, committees, auditors, and regulators.

Nice To Haves

Degree or concentration in a quantitative field such as mathematics, physics, engineering, computer science, or financial engineering.
Experience interacting with senior management, regulators, auditors, or governance committees.
Experience managing third-party validation providers and model-remediation efforts.
Risk-management-related certification or a principles-based certification demonstrating mastery of a relevant framework or methodology.

Responsibilities

Maintain and enhance the Bank’s MRM Framework, including governance policies, standards, procedures, methodologies, and reporting.
Serve as the independent second-line subject matter expert on model risk management across the Company and provide effective challenge of model design, assumptions, limitations, implementation, data quality, performance, and use.
Maintain the enterprise model inventory and model risk classification methodology, ensuring models are fully and accurately identified and risk-tiered.
Oversee governance of quantitative models used for credit risk, CECL, capital planning, liquidity risk management, asset liability management, stress testing, fraud monitoring, marketing analytics, pricing, forecasting, and other business purposes.
Perform independent validations and periodic model reviews using regulatory guidance and industry-accepted methodologies.
Manage third-party validation providers, including engagement oversight, quality review, challenge of conclusions, and issue remediation.
Evaluate conceptual soundness, methodology, data integrity, implementation accuracy, process controls, assumptions, limitations, outcomes analysis, benchmarking, sensitivity testing, stress testing, and back-testing.
Assess model findings, limitations, and compensating controls; ensure remediation plans are developed and monitored through completion.
Present validation results and recommendations to management committees, senior management, Audit, and regulators.
Oversee governance throughout model development, acquisition, implementation, use, monitoring, change management, redevelopment, decommissioning, and retirement.
Review proposed model changes and determine validation requirements and risk implications; monitor performance and usage trends for deterioration, emerging risks, control weaknesses, or changing business conditions.
Oversee governance and risk assessment for vendor-developed models, externally sourced methodologies, and significant analytical tools used in business decisions.
Evaluate model documentation, vendor assumptions, implementation controls, performance-monitoring practices, and independent-review requirements; apply enhanced governance to significant tools that may not meet the regulatory definition of a model.
Chair the Model Risk Management Committee and develop and present periodic reporting on inventory status, validation activity, findings, remediation, emerging risks, and program effectiveness.
Coordinate with regulators, Internal Audit, external auditors, and consultants on model risk management matters; monitor regulatory developments and industry practices and recommend program enhancements.
Assist with second-line oversight of data governance, data quality, and data controls supporting models.
Assess completeness, accuracy, lineage, governance, and controls surrounding model inputs and outputs; review data sourcing, transformations, reconciliations, and key assumptions for critical models.
Collaborate with business units, Technology, and governance functions to identify and remediate data-related risks affecting model reliability and decision-making.
Support enterprise initiatives involving data governance, model data quality, and analytical risk management.
Oversee second-line governance and independent review of AI, machine learning, generative AI, predictive analytics, and other advanced analytical solutions.
Assess governance, validation, testing, monitoring, transparency, explainability, data quality, bias, and outcome validation for AI-enabled solutions.
Support enterprise standards for the acquisition, deployment, monitoring, and retirement of AI-enabled technologies.
Coordinate with Information Security, Data Governance, Compliance, Legal, Operational Risk, and business-line partners on risk practices throughout the lifecycle of AI-enabled solutions.
Oversee third-party staff supporting model validation; the role may supervise staff if validations transition in-house.

Benefits

Incentive eligibility based on company, business, and/or individual goal achievement and performance.
Paid Time Off (PTO).
Paid holidays.
Medical, dental, and vision plans.
Flexible Spending Accounts (FSA) and Health Savings Accounts (HSA).
401(k) retirement plan.
Disability insurance.
Employee Assistance Program (EAP).
Basic life insurance.
Company-sponsored tuition disbursement and loan repayment programs.
Voluntary supplemental life insurance, accident, critical illness, hospital indemnity, and legal plans.