Model Risk Management Quantitative Analyst

Wright-Patt Credit Union Inc.; Beavercreek (OH); On-site; USD 110,000 - 150,000; Full time

Job summary

Wright-Patt Credit Union Inc. is seeking a Model Risk Management Quantitative Analyst to independently assess models used across lending, financial management, forecasting, fraud detection, liquidity management, and strategy.

You will perform quantitative testing, data analysis, performance evaluation, and independent challenge, partnering with model owners and risk leaders to ensure models are sound, well implemented, and fit for purpose.

Qualifications

• Independent validation of risk and lending models.
• Experience with model risk governance.
• Ability to communicate results to stakeholders.

Responsibilities

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

Skills

Quantitative analysis

Statistical techniques

Data analysis

Tools

Python

R

SQL

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.