Head of Product Data & Analytics - Supply Chain Digital Enablement

The Coca-Cola Company•Atlanta, GA

1d•Onsite

About The Position

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.

Requirements

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
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
Fluency 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.
Applicants must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company's sponsorship to continue to work legally in the United States.

Nice To Haves

Advanced degree in data science, statistics, economics, computer science, or a related field preferred.
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
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