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  

• Partner with Product and Engineering to embed predictive, adaptive, and intelligent capabilities into product experiences  

• Ensure models are monitored, evaluated, explained, and continuously improved 

Elevate data capability across the organization 

• Coach PMs, designers, engineers, and business partners to become more confident, data-literate decision-makers  

• Make analytics, experimentation, and learning a routine part of product team practice  

• Share insights broadly to build organizational knowledge and improve portfolio-level decision-making 

Influence product strategy and portfolio decisions 

• Size opportunities, prioritize bets, and guide investment decisions using data  

• Provide scenario modeling, forecasting, and evidence to inform portfolio sequencing  

• Partner with product leadership to ensure strategy is grounded in customer behavior, business value, and measurable outcomes 

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 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 

Preferred Qualifications 

• 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, modeling, experimentation, workflow automation, or product decisioning 

Education: Bachelor's degree required; Advanced degree in data science, statistics, economics, computer science, or a related field preferred. 

Skills 

• 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, workflows, and business goals to understand how products create value across teams and platforms.  

• Experimentation expertise: Designs and governs experiments that produce reliable evidence and help teams reduce risk and accelerate learning. 

• Data science fluency: Guides advanced analytics and modeling approaches that deliver insight, decision support, and product value. 

• Insight storytelling and influence: Translates complex analyses into clear narratives that shape strategy, inform decisions, and align stakeholders. 

• Team leadership and capability building: Develops strong analytics and data science talent while building a culture of curiosity, rigor, and shared ownership of outcomes.