Model Risk Management Quantitative Analyst

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.

1)      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.

2)      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.

3)      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.

4)      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. 

5)      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.

1)      Bachelor’s degree in Business, Mathematics, Analytics, Computer Science, Engineering or related field. Masters Degree preferred.

2)      5+ years of experience in analytics, data, consulting, or related roles with demonstrated ability to conduct advanced statistical analysis.

3)      Advanced proficiency in SQL for building datasets, validating results, and enabling scalable analysis.

4)      Strong proficiency in Python for analysis and automation (pandas, NumPy; experience building reusable workflows).

5)      Strong statistical foundation including hypothesis testing, regression, sampling, and experimental design concepts.

6)      Experience with quantitative model validation, model development, risk analytics, or statistical modeling.

7)      Knowledge of model lifecycle management, model performance monitoring, and validation techniques.

8)      Understanding of financial services models including CECL, credit risk, ALM, liquidity, fraud, forecasting, or operational models.

9)      Ability to independently analyze model assumptions and assess reasonableness.

10)  Experience creating executive-level dashboards and visuals in Power BI (or similar tools).