Head of Product Data & Analytics

The Coca-Cola Company•Atlanta, GA

21h•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
Analytical rigor: Applies strong statistical and analytical judgment to define, measure, and interpret product outcomes with clarity and precision.
Product and systems thinking: Connects data, behavior, and business goals; understands how metrics and models influence decisions across journeys, platforms, and teams.
Experimentation expertise: Designs and governs experiments that produce reliable, decision-ready evidence and helps teams reduce risk and accelerate learning.
Data science fluency: Guides analysts and data scientists in applying advanced techniques such as segmentation, forecasting, clustering, and recommendations to deliver insight and customer value.
Insight storytelling and influence: Translates complex analyses into clear, compelling narratives that shape strategy, inform decisions, and align cross-functional stakeholders.
Team leadership and capability building: Develops, coaches, and elevates analysts and data scientists; builds a culture of curiosity, rigor, and shared ownership of outcomes across product teams.

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