Director, Lead Product Data Analyst - Digital

Coca-Cola•Atlanta, GA

About The Position

The Coca-Cola Company is hiring a Lead Product Data Analyst for its Digital Product Data & Analytics team within the North America Digital & Data organization. The role reports to the Head of Product Data & Analytics and works with product data analysts and data scientists embedded in cross-functional squads alongside Product, Design, Engineering, Research, and business stakeholders. The analyst will help define product outcomes, develop and test hypotheses, and influence product-roadmap priorities and trade-offs. Working autonomously in complex and ambiguous problem spaces, the role applies advanced analytics, product thinking, and creative problem-solving to strengthen product analytics across the enterprise. The position focuses on defining product measurement strategies and success metrics; analyzing product usage, funnels, customer journeys, and segments; and applying statistical methods and experimentation to understand performance and quantify impact. It also defines and improves instrumentation, event tracking, data structures, and data-quality standards, partnering with engineering and architecture teams to ensure product data flows into the data lake. The role produces dashboards, visualizations, and data narratives that turn complex findings into actionable recommendations and promote thoughtful, ethical, high-quality use of data. The position is based in Atlanta, United States, with 0%–25% travel and relocation provided. Applicants must already be authorized to work full-time in the United States and must not require The Coca-Cola Company's employment-based visa sponsorship.

Requirements

Bachelor’s or Master’s degree in analytics, statistics, biostatistics, data science, economics, computer science, or a related quantitative field, or equivalent practical experience.
At least 7 years of experience in advanced analytics or applied data science supporting digital products in B2B or B2C marketplaces.
Strong foundation in experimental design, causal inference, and statistical methods.
Advanced proficiency in SQL and Python or R.
Experience working with large, complex datasets using Microsoft Azure technologies such as Synapse, Fabric, and PySpark notebooks.
Proven ability to collaborate with cross-functional teams and influence product strategy and decisions.
Strong communication skills and ability to translate complex analyses into clear insights and recommendations.
Ability to operate independently in ambiguous environments and foster a data-driven culture of learning and growth.
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 working legally in the United States.

Nice To Haves

Experience supporting B2B or multi-sided marketplace products.
Experience applying advanced analytics and causal-inference methods to B2B product data.
Experience with product instrumentation and experimentation tools such as Amplitude, Mixpanel, FullStory, or GA4.

Responsibilities

Define and evolve product measurement strategies and success metrics across adoption, engagement, retention, product health, and value creation.
Analyze product usage, funnels, customer journeys, and segments to uncover user behavior, friction points, trends, and opportunities.
Apply advanced analytics, statistical methods, A/B testing, incrementality testing, and quasi-experimental analysis to evaluate product performance and quantify impact.
Partner with Product, Design, Engineering, Data, Commercial, and Operations to translate insights into product strategy, priorities, and action.
Develop scalable measurement frameworks, playbooks, and experimentation standards that strengthen analytical maturity and outcome-based decision-making.
Define and improve product instrumentation, event tracking, and data structures to accurately capture user behavior and support measurement.
Partner with Application Engineering to implement and validate event instrumentation.
Partner with Data Architecture and Data Engineering to define data requirements and ensure product data flows into the data lake.
Establish and maintain consistent metric definitions, data-quality standards, and analytical assumptions across products.
Develop dashboards, visualizations, and data narratives that make insights accessible and actionable.
Translate complex quantitative findings into clear recommendations that influence product strategy, prioritization, and decisions.
Champion thoughtful, ethical, and high-quality use of data across the product organization.