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Added 1 min ago
Director, Sales Enablement - Business Insurance, Select and BI Field
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…Leadership, Sales Leadership, Product Management, and Sales Insights/Analytics to influence and drive Sales strategies that create a competitive advantage and drive…
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Added 21 mins ago
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Added 31 mins ago
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Cloud Solution Architect - AI Apps
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Added 36 mins ago
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…complex and diverse software systems (e.g., processing-intensive analytics, novel algorithm development, manipulation of extremely large data sets, real-time systems, and business…
Added 36 mins ago
Software Engineer, Level 1
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…complex and diverse software systems (e.g., processing-intensive analytics, novel algorithm development, manipulation of extremely large data sets, real-time systems, and business…
Added 36 mins ago
Software Engineer, Level 2
Avalore, LLC
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…complex and diverse software systems (e.g., processing-intensive analytics, novel algorithm development, manipulation of extremely large data sets, real-time systems, and business…

Wright-Patt Credit Union Inc.

Model Risk Management Quantitative Analyst

Beavercreek, OH, United States

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Job details

Experience: 2) 5+ years of experience in analytics, data, consulting, or related roles with demonstrated ability to conduct advanced statistical analysis.
Posting language: English
Posted: Nov 7, 2026

Original 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.

Required Skills

Bachelor’s degree in Business, Mathematics, Analytics, Computer Science, Engineering or related field. Masters Degree preferred.
5+ years of experience in analytics, data, consulting, or related roles with demonstrated ability to conduct advanced statistical analysis.
Advanced proficiency in SQL for building datasets, validating results, and enabling scalable analysis.
Strong proficiency in Python for analysis and automation (pandas, NumPy; experience building reusable workflows).
Strong statistical foundation including hypothesis testing, regression, sampling, and experimental design concepts.
Experience with quantitative model validation, model development, risk analytics, or statistical modeling.
Knowledge of model lifecycle management, model performance monitoring, and validation techniques.
Understanding of financial services models including CECL, credit risk, ALM, liquidity, fraud, forecasting, or operational models.
Ability to independently analyze model assumptions and assess reasonableness.
Experience creating executive-level dashboards and visuals in Power BI (or similar tools).

Required Experience

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More about this job

Skills

Risk management
Financial management
Forecasting
Fraud detection
Liquidity management
Decision making
Computer science
Statistics
SQL
Python
pandas
NumPy
Microsoft Power BI

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