Head of Product Data & Analytics

Coca-Cola•Atlanta, GA

7d•Hybrid

About The Position

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.

Requirements

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 modeling, and recommendations
Comfort with modern data platforms including warehouses, event tracking, BI tools, and experimentation frameworks
Ability to translate complex analyses into clear, actionable insights for product and executive audiences
Strong collaboration and influence skills across Product, Engineering, and Design
Bachelor's degree; Advanced degree in data science, statistics, economics, computer science, or a related field preferred.

Nice To Haves

Experience building or scaling data and analytics within empowered product team models
Background applying causal inference or quasi-experimental methods in real-world environments
Exposure to embedding ML models into customer-facing products
Familiarity with AI and agentic systems as accelerators for analysis or modeling

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