Head of Product Data & Analytics

Job Description Summary:

The Coca-Cola Company is building a modern product organization for a business that operates at extraordinary scale. We are building the digital product foundation for how we serve customers, enable teams, and grow the business in the years ahead. Data and analytics are central to that ambition, helping teams understand what is happening, why it matters, and where to act next. 

This role will help build the intelligence layer behind our product organization: the measurement, experimentation, analytics, data science, and decision-support capabilities that help teams learn faster, make better decisions, and create measurable customer and business value. 

About the Role 

The Head of Product Data & Analytics leads the data discipline within the Product organization, overseeing analysts and data scientists embedded in empowered product teams. 

You will build and scale a modern product insights capability that connects product analytics, experimentation, instrumentation, decision support, and data science. Working closely with Product, Design, and Engineering, you’ll help teams connect what users do with why they do it, and translate those insights into better products, stronger decisions, and measurable outcomes. 

This is a cross-functional leadership role focused on shaping how Coca-Cola’s product teams learn, prioritize, and create value through data. 

Responsibilities 

Build and lead the Product Data & Analytics practice 

• Hire, develop, and lead analysts, data scientists, and experimentation specialists embedded in product teams  

• Define roles, standards, tools, operating rhythms, and career paths for analytics and data science within the product organization  

• Build a culture rooted in curiosity, rigor, clear storytelling, and shared ownership of outcomes 

Make data foundational to how product teams work 

• Ensure teams use data to understand user behavior, measure outcomes, evaluate ideas, and identify new opportunities  

• Help product leaders move from feature roadmaps to outcome-based KPIs, scorecards, and learning agendas  

• Partner with Design and Research to connect behavioral data, qualitative insight, and business context 

Define measurement, instrumentation, and experimentation 

• Establish KPIs, guardrails, leading indicators, and measurement frameworks for each product area  

• Ensure products are instrumented effectively so teams can understand adoption, engagement, friction, and impact  

• Operationalize experimentation practices, including A/B tests, holdouts, causal inference, and other methods appropriate to real-world product environments 

Lead core product analytics capabilities 

• Oversee user analytics, customer analytics, funnels, cohorts, retention, adoption, and behavioral analyses  

• Guide business analytics such as lifetime value, churn, economics, and value realization  

• Ensure data quality, accuracy, consistency, and usability across product platforms and reporting environments 

Develop and apply data science for insight and product value 

• Guide segmentation, forecasting, clustering, propensity modeling, recommendations, and other advanced analytics approaches