Quantitative Analyst

Department

Data and Analytics

Job Location

3560 Pentagon Blvd, Beavercreek, Ohio

Additional Locations

• Cincinnati, Ohio
• Columbus, Ohio

Full-Time/Regular

Work Type

Variable

NMLS Required

No

Pay Classification

Exempt (Salary)

Grade and Compensation Band

PG 18: $84,427.20 – $126,568.00 (Annually)

Target Compensation

$90,000 - $115,000

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.

• High-Impact Quantitative Analysis & Decision Science (30%): Use statistical methods to identify drivers of performance, validate hypotheses, and quantify the impact of business decision using structured and repeatable approaches.
• 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.
• Experimentation, Testing, and Impact Evaluation (25%): Design measurement frameworks that ensure the organization can track initiative performance, quantify impact, and drive accountability.
• 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.
• Predictive Analytics & Optimization (20%): Drive advanced analytics efforts that improve targeting, prioritization, and decision-making through modeling and quantitative scoring.
• 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.
• Automation & Scalable Analytics Delivery (15%): Increase speed, consistency, and reliability of insights by automating analysis workflows and enabling scalable analytics delivery.
• 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.
• Communication, Visualization, and Executive Enablement (10%): Present actionable insights to senior leadership in a format that is relevant for the audience.
• 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.

• Specialized or Technical Knowledge and 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 experimentation and impact evaluation (A/B testing, incremental lift, pre/post comparisons).
• Experience creating executive-level dashboards and visuals in Power BI (or similar tools).
• Strong understanding of KPI design, metric governance, and measurement best practices.