Head of Product Data & Analytics - Supply Chain Digital Enablement

Job Description Summary:

The Coca Cola Company is transforming how its North America Supply Chain operates, using digital products to enable a supply chain that moves at the speed of the market. Our work connects planning, sourcing, manufacturing, and fulfillment into a responsive, reliable, and continuously improving network—one that can adapt quickly to change while operating at global scale.

Our product organization is built on small, empowered teams that move with clarity and purpose, making digital a true source of competitive advantage. Data and Analytics are a core partner to Product, Engineering and Design - shaping how decisions are made and value is delivered through insight, experimentation, and measurement. If you’re excited to help build this practice and define from the ground up, we’d love to meet you.

About the Role

The Head of Product Data & Analytics, Supply Chain Digital Enablement (North America) 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

• Enable product leaders to 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, including service levels, forecast accuracy, throughput, inventory health, and cost‑to‑serve

• 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 and product economics analytics such as LTV, churn, and unit 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 product 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

• Scale learnings and insights across the organization to build shared 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

• Bachelor's degree in data science, statistics, economics, computer science, or related field

• Experience embedding analysts and/or data scientists within cross-functional product or engineering teams

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