Quantitative Analyst

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.

• 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.

• 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.