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 RoleThe 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.ResponsibilitiesBuild and lead the Data & Analytics practiceHire, develop, and lead analysts, data scientists, and experimentation specialists embedded in product teamsDefine roles, standards, and career paths for analytics and data scienceCreate a culture rooted in curiosity, rigor, and clear storytellingMake data foundational to product discovery and deliveryEnsure teams use data to understand behavior, measure outcomes, and evaluate ideasGuide the use of experiments, prototypes, and causal analysis to reduce riskEnable product leaders to shift from feature roadmaps to outcome-based KPIs and scorecardsDefine measurement, instrumentation, and experimentationEstablish KPIs, guardrails, and leading indicators for each product area, including service levels, forecast accuracy, throughput, inventory health, and cost‑to‑serveOperationalize experimentation practices including A/B tests, holdouts, and causal inferenceEnsure products are instrumented correctly so teams are never “flying blind”Lead core product analytics capabilitiesOversee user analytics, customer analytics, funnels, cohorts, and retention analysesGuide business and product economics analytics such as LTV, churn, and unit economicsEnsure data quality, accuracy, and usability across platformsDevelop and apply data science for insight and customer valueGuide segmentation, forecasting, clustering, and propensity modelingPartner with product and engineering to embed predictive and adaptive models into product experiencesEnsure ML models are monitored, evaluated, and continuously improvedElevate data capability across the organizationCoach PMs, designers, and engineers to be confident, data-literate decision-makersPromote experimentation and analytics as routine parts of product workScale learnings and insights across the organization to build shared knowledgeInfluence product strategy and portfolio decisionsSize opportunities, prioritize bets, and guide investment decisions using dataProvide scenario modeling and forecasting for portfolio sequencingRepresent the data and insights perspective in senior forumsKey Qualifications10+ years of experience in analytics, data science, or related fields, with at least five years leading teams in digital product environmentsBachelor's degree in data science, statistics, economics, computer science, or related fieldExperience embedding analysts and/or data scientists within cross-functional product or engineering teamsStrong foundation in product analytics including behavioral data, funnels, cohorts, and retentionDeep experience with experimentation including A/B testing, test design, and interpretationFamiliarity with data science techniques such as clustering, regression, propensity modeling, and recommendationsFluency with modern data platforms including warehouses, event tracking, BI tools, and experimentation frameworksAbility to translate complex analyses into clear, actionable insights for product and executive audiencesStrong collaboration and influence skills across Product, Engineering, and DesignPreferred QualificationsAdvanced degree in data science, statistics, economics, computer science, or a related field preferred.Experience building or scaling data and analytics within empowered product team modelsBackground applying causal inference or quasi-experimental methods in real-world environmentsExposure to embedding ML models into customer-facing productsFamiliarity with AI and agentic systems as accelerators for analysis or modelingSkillsAnalytical 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 stor