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%): Use statistical methods to identify drivers of performance, validate hypotheses, and quantify the impact of business decision using structured and repeatable approaches.
a) Perform exploratory data analysis, segmentation, and trend analysis to uncover patterns and anomalies.
b) Apply statistical techniques such as hypothesis testing, confidence intervals, correlation, and regression analysis.
c) Identify opportunities for growth, efficiency, and experience improvement using data-backed recommendations.
d) Deliver decision-ready outputs that connect analysis to actions, tradeoffs, and expected outcomes.
2) Experimentation, Testing, and Impact Evaluation (25%): Design measurement frameworks that ensure the organization can track initiative performance, quantify impact, and drive accountability.
a) Support A/B testing and experiment analysis including test design inputs, lift measurement, and interpretation.
b) Partner with product and business teams to define success metrics, baselines, and measurement plans.
c) Evaluate initiative effectiveness using controlled comparisons, pre/post analysis, and statistical significance testing.
d) Develop standardized experiment readouts and decision frameworks to improve speed and consistency.
3) Predictive Analytics & Optimization (20%): Drive advanced analytics efforts that improve targeting, prioritization, and decision-making through modeling and quantitative scoring.
a) Partner with data scientists to support model development by preparing datasets, validating features, and interpreting outputs.
b) Build and maintain scoring frameworks (propensity, prioritization, classification support) aligned to business use cases.
c) Support model evaluation using practical performance measures (lift, precision/recall, error rates).
d) Translate model outputs into actionable recommendations and operational workflows.
4) Automation & Scalable Analytics Delivery (15%): Increase speed, consistency, and reliability of insights by automating analysis workflows and enabling scalable analytics delivery.
a) Develop automated analysis workflows using SQL and Python to reduce manual effort.
b) Build reusable scripts, templates, and standardized datasets to improve reliability and consistency.
c) Partner with data engineering teams to improve data availability and support repeatable pipelines.
d) Implement monitoring and alerting for key performance indicators and threshold-based changes.
5) Communication, Visualization, and Executive Enablement (10%): Present actionable insights to senior leadership in a format that is relevant for the audience.
a) Build clear, executive-ready summaries and visualizations tied to business outcomes.
b) Present findings and recommendations to senior leaders and cross-functional teams.
c) Communicate confidence levels, limitations, and tradeoffs in a practical way.
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.
Required Skills
A. Specialized or Technical Knowledge and Skills:
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 experimentation and impact evaluation (A/B testing, incremental lift, pre/post comparisons).
7) Experience creating executive-level dashboards and visuals in Power BI (or similar tools).
8) Strong understanding of KPI design, metric governance, and measurement best practices.
