Quantitative Analyst

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

Job summary

The Quantitative Analyst role at Wright-Patt Credit Union Inc. focuses on leading high-impact statistical analysis, measurement design, and scalable analytics to improve business performance and decision-making.

You will partner with Strategy, Product, and Technology to evaluate initiatives, design robust measurement plans, and deliver executive-ready insights. Responsibilities include supporting A/B testing, building predictive analytics, automating workflows with SQL and Python, and creating

Qualifications

• Lead high-impact quantitative analysis to inform decisions.
• Design experiments and measure impact using controlled methods.
• Develop predictive analytics and scoring frameworks.
• Automate analyses with scalable SQL/Python workflows.
• Create executive-ready visuals and summaries for leadership.

Responsibilities

• Perform exploratory data analysis, segmentation, and trend analysis.
• Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.
• Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.
• Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.
• Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.
• Partner with product and business teams to define success metrics, baselines, and measurement plans.
• Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.
• Develop standardized experiment readouts and decision frameworks to improve speed and consistency.
• Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.
• Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.
• Support model evaluation using practical performance measures (lift, precision/recall, error rates).
• Translate model outputs into actionable recommendations and operational workflows.
• Develop automated analysis workflows using SQL and Python to reduce manual effort.
• Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.
• Partner with data engineering teams to improve data availability and support repeatable pipelines.
• Implement monitoring and alerting for key performance indicators and threshold-based changes.
• Build clear, executive-ready summaries and visualizations tied to business outcomes.
• Present findings and recommendations to senior leaders and cross-functional teams.
• Communicate confidence levels, limitations, and tradeoffs in a practical way.
• 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.

Skills

Statistical analysis

Experimentation

A/B testing

Python

SQL

Data visualization

Job description

The Quantitative Analyst is responsible for leading high-impact statistical analysis, measurement design, and scalable analytics solutions that improve business performance and decision-making. This role partners closely with Strategy, Product, and Technology teams to evaluate key initiatives, identify performance drivers, develop statistically sound measurement approaches, and deliver executive-ready insights that influence priorities and investments. The Quantitative Analyst combines strong analytical depth with automation and repeatability, ensuring insights are accurate, timely, and operationally useful.

1) High-Impact Quantitative Analysis & Decision Science (30%)

• Perform exploratory data analysis, segmentation, and trend analysis to uncover patterns and anomalies.
• Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.
• Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.
• Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.

2) Experimentation, Testing, and Impact Evaluation (25%)

• Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.
• Partner with product and business teams to define success metrics, baselines, and measurement plans.
• Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.
• Develop standardized experiment readouts and decision frameworks to improve speed and consistency.

3) Predictive Analytics & Optimization (20%)

• Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.
• Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.
• Support model evaluation using practical performance measures (lift, precision/recall, error rates).
• Translate model outputs into actionable recommendations and operational workflows.

4) Automation & Scalable Analytics Delivery (15%)

• Develop automated analysis workflows using SQL and Python to reduce manual effort.
• Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.
• Partner with data engineering teams to improve data availability and support repeatable pipelines.
• Implement monitoring and alerting for key performance indicators and threshold-based changes.

5) Communication, Visualization, and Executive Enablement (10%)

• Build clear, executive-ready summaries and visualizations tied to business outcomes.
• Present findings and recommendations to senior leaders and cross-functional teams.
• Communicate confidence levels, limitations, and tradeoffs in a practical way.
• 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.