Model Risk Management Quantitative Analyst

Wright-Patt Credit Union; Beavercreek (OH); On-site; USD 90,000 - 130,000; Full time

Job summary

Wright-Patt Credit Union seeks a Model Risk Management Quantitative Analyst to independently validate models used across lending, forecasting, risk management, and strategic decision-making. The role involves testing, data analysis, performance evaluation, and providing independent challenge to ensure models are sound and fit for purpose.

You will partner with model owners, data teams, and risk leaders to assess assumptions, identify limitations, and communicate findings that support effective

Qualifications

• Independent validations of internally developed and vendor-provided models.
• Data analysis and quantitative assessment.
• Model monitoring and performance evaluation.
• Documentation and governance support.
• Communication and stakeholder partnership.

Responsibilities

• Perform independent validations of models; review purpose, design, methodology, and limitations.
• Analyze input data, run exploratory analyses, and apply statistical techniques to evaluate performance.
• Review monitoring results, assess stability, and recommend improvements.
• Prepare validation reports and support model risk assessments and policy compliance.
• Present findings to model owners and management with clear, evidence-based rationale.

Job description

The Model Risk Management Quantitative Analyst is responsible for independently assessing models used throughout the credit union to support lending, financial management, forecasting, fraud detection, liquidity management, and strategic decision-making. This role performs quantitative testing, data analysis, performance evaluation, and independent challenge to ensure models are conceptually sound, appropriately implemented, and fit for purpose. They partner with model owners, data teams, business stakeholders, and risk management leaders to evaluate model assumptions, assess performance, identify limitations, and communicate findings that support sound model risk management practices.

Independent Model Validation & Testing (35%)

• a) Perform independent validations of internally developed and vendor-provided models.
• b) Review model purpose, design, methodology, assumptions, limitations, and intended use.
• c) Assess conceptual soundness and alignment with business objectives.
• d) Conduct replication testing, benchmarking, outcomes analysis, and sensitivity testing
• e) Evaluate performance against expected results and established thresholds.
• f) Identify model weaknesses, limitations, and areas requiring remediation.

Data Analysis & Quantitative Assessment (25%)

• a) Analyze model input data for accuracy, completeness, consistency, and appropriateness.
• b) Perform exploratory analysis to identify data anomalies and performance drivers.
• c) Apply statistical techniques such as:
  • i. Regression analysis
  • ii. Hypothesis testing
  • iii. Correlation analysis
  • iv. Segmentation analysis
  • v. Stability analysis
• d) Evaluate model effectiveness using appropriate performance metrics.

Model Monitoring & Performance Evaluation (20%)

• a) Review ongoing model monitoring results.
• b) Assess model stability and performance trends.
• c) Evaluate performance deterioration and emerging model risks.
• d) Analyze back-testing and forecasting results.
• e) Recommend model enhancements and monitoring improvements.

Documentation & Governance Support (10%)

• a) Prepare validation reports documenting scope, testing, findings, conclusions, and recommendations.
• b) Support model inventory maintenance and model risk assessments.
• c) Ensure validation activities comply with regulatory guidance and internal model risk standards.
• d) Ensures proper policies, procedures, risk mitigation activities, and operating controls are followed. Reports gaps in policies, procedures, and operating controls to leadership to ensure member impact and risk is mitigated.

Communication & Stakeholder Partnership (10%)

• a) Present findings to model owners and management.
• b) Communicate technical issues in a clear and practical manner.
• c) Provide effective challenge supported by quantitative evidence.