Head of Product Data & Analytics

Description

Summary :

This is a People Leader role. The incumbent must be based in Atlanta, GA and work a hybrid work schedule

Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience. Our product organization brings together small, empowered teams that move with clarity, speed, and purpose, enabling digital to be a meaningful source of advantage across our operating unit.

Our work touches on the experiences that keep the business running, including customer journeys, service delivery, sales workflows, and the systems that connect them. We are raising our standards for product craft and rebuilding the platforms behind these experiences.

About the Role

The Head of Product Data & Analytics leads the data discipline within the Product organization, overseeing the analysts and data scientists embedded in empowered product teams. This leader is responsible for how teams use data to understand behavior, measure progress, experiment confidently, and discover new opportunities.

You will build and scale a modern product insights capability that brings together analytics, data science, experimentation, instrumentation, and decision support. You will ensure teams move from opinion-driven to evidence-informed, while partnering closely with Design and Research to connect what users do with why they do it.

This role is deeply cross-functional. You will work alongside Product, Design, and Engineering leaders to define metrics, build measurement frameworks, instrument features, run experiments, and develop models that create both internal insight and customer-facing value.

Responsibilities Build and lead the Data & Analytics practice

• Hire, develop, and lead analysts, data scientists, and experimentation specialists embedded in product teams
• Define roles, standards, and career paths for analytics and data science
• Create a culture rooted in curiosity, rigor, and clear storytelling

Make data foundational to product discovery and delivery

• Ensure teams use data to understand behavior, measure outcomes, and evaluate ideas
• Guide the use of experiments, prototypes, and causal analysis to reduce risk
• Help product leaders shift from feature roadmaps to outcome-based KPIs and scorecards

Define measurement, instrumentation, and experimentation

• Establish KPIs, guardrails, and leading indicators for each product area
• Operationalize experimentation practices including A / B tests, holdouts, and causal inference
• Ensure products are instrumented correctly so teams are never “flying blind”

Lead core product analytics capabilities

• Oversee user analytics, customer analytics, funnels, cohorts, and retention analyses
• Guide business analytics such as LTV, churn, and economics
• Ensure data quality, accuracy, and usability across platforms

Develop and apply data science for insight and customer value

• Guide segmentation, forecasting, clustering, and propensity modeling
• Partner with product and engineering to embed predictive and adaptive models into experiences
• Ensure ML models are monitored, evaluated, and continuously improved

Elevate data capability across the organization

• Coach PMs, designers, and engineers to be confident, data-literate decision-makers
• Promote experimentation and analytics as routine parts of product work
• Share learnings and insights broadly to create organizational knowledge

Influence product strategy and portfolio decisions

• Size opportunities, prioritize bets, and guide investment decisions using data
• Provide scenario modeling and forecasting for portfolio sequencing
• Represent the data and insights perspective in senior forums

Key Qualifications

• 10+ years of experience in analytics, data science, or related fields, with at least five years leading teams in digital product environments
• Experience embedding analysts and / or data scientists within cross-functional product or engineering teams
• Strong foundation in product analytics including behavioral data, funnels, cohorts, and retention
• Deep experience with experimentation including A / B testing, test design, and interpretation
• Familiarity with data science techniques such as clustering, regression, propensity…