Job Description Summary:
At The Coca-Cola Company, data is a strategic asset that powers personalized experiences, accelerates decision-making, and enables sustainable growth in a digital-first world. As part of the North America Operating Unit (NAOU) Digital & Data organization, the Data Analytics & Engineering team is responsible for delivering scalable data, analytics, and AI capabilities that empower the business with trusted insights and intelligent decision support.
The Senior Director, AI & Analytical Engineering will lead a multidisciplinary team to build scalable AI-first capabilities, applications, and frameworks across the North America Operating Unit. This leader will establish a governed AI framework and federated enablement model that helps core functions solve advanced analytical problems and improve enterprise decision-making.
With deep knowledge of the enterprise data foundation and business priorities, this leader will define a clear vision for scalable AI-enabled analytics. Partnering across business functions, Global Data, and Data & Insights, the role will deliver differentiated solutions for stakeholders from data scientists to sales VPs. The leader will scale current capabilities while advancing agentic analytics as an innovation frontier, embedding AI across exploration, analysis, modeling, product design, development, testing, and documentation with human accountability and technical rigor.
Accountable for portfolio priorities, capability roadmaps, delivery standards, user experience, and high-value outcomes, the Senior Director will serve as the enterprise authority on analytical data use and AI integration. This hands-on leader will challenge legacy mindsets, redesign processes around AI, establish rigorous evaluation and control mechanisms, drive continuous improvement, grow the team, spur innovation, and earn senior-leader trust.
What You’ll Do for Us
Lead AI, Agentic Analytics, and Decision Capabilities
- Establish the vision, framework, priorities, and roadmap for using AI, agentic analytics, and advanced analytics to improve enterprise decisions, working closely with AI strategy lead
- Build reusable analytical, AI, agentic, application, and governance patterns rather than isolated models or tools.
- Prioritize AI use cases that shorten the question-to-insight cycle, raise insight quality, and multiply capacity for advanced analytics, machine learning, visualization, and storytelling.
- Embed directly with business functions to co-design and scale agentic AI tools that accelerate delivery for the immediate business team while building reusable capabilities for the broader enterprise.
- Triage incoming business priorities and identify the right technical solution—whether it requires AI or not.
Establish a Scaled Enablement and Change Model
Lead a flexible delivery model based on business need, complexity, risk, and functional capability:
- Self-Service: Provide governed data, tools, frameworks, reusable components, enablement resources, and guardrails that allow functional builders to create independently.
- Co-Build: Combine central technical expertise with functional knowledge to jointly design and deliver solutions while growing partner capability.
- Fully Owned: Lead delivery end to end when work is highly complex, cross-functional, strategically important, or beyond a partner team’s capacity.
Ensure all three levels follow shared standards and contribute to a coherent enterprise foundation. Lead the transformational change required to make AI-first analytics how work gets done. Define differentiated delivery and adoption approaches, partnering with functional and change leaders on communications, training, readiness, and sustained use.
Build AI, Analytics, Measurement, and Decision Products
- Partner closely with the Data Foundation leader and data product teams to understand and appropriately use governed pipelines, curated datasets, semantic layers, and business-ready data assets in AI, analytics, measurement, and decision products.
- Deliver dashboards, scorecards, executive reporting, self-service analytics, and single-pane decision experiences that make trusted data easy to access and use.
- Define and operationalize KPI strategies, business metrics, measurement frameworks, and performance scorecards across brands, categories, channels, and initiatives.
- Automate reporting, workflows, alerts, and decision-support processes to reduce manual effort and improve speed, consistency, and scale.
- Lead the portfolio of internal and vendor-led initiatives and partner with Data Foundation, Technology, Architecture, Governance, and Operations to scale and sustain capabilities with appropriate AI controls.
Lead People and Influence the Enterprise
- Build, lead, and grow a high-performing, multidisciplinary team of AI engineers and architects with diverse backgrounds and complementary skill sets.
- Lead by example through technical rigor, creativity, curiosity, candor, and direct engagement in the work.
- Challenge direct reports to leverage their strengths and drive innovation with AI, delegating work with purpose to support individual and team growth.
- Recruit, coach, and develop technical leaders and emerging talent while establishing clear standards, accountability, ownership, and career paths.
- Serve as a trusted advisor to senior leaders by translating complex possibilities into clear choices, risks, tradeoffs, and recommendations.
What Success Looks Like
- High-value business outcomes enabled by a faster path from business questions to high-quality insights and decisions.
- AI multiplying the reach and quality of advanced analytics, machine learning, visualization, and storytelling.
- Agentic AI tools embedded in business workflows and delivering faster, higher-quality analysis and decisions.
- Functional builders operating effectively within a shared framework.
- Internal and vendor-led initiatives operating as one coherent portfolio.
- A team recognized for technical excellence, creativity, innovation, and measurable value.
- Senior leaders viewing the role as a trusted advisor on AI and the analytical use of data.
Required Qualifications & Experience
- Bachelor’s degree required.
- 15+ years of progressive experience across enterprise data, business intelligence, advanced analytics, data science, machine learning, decision science, analytical products, application development, or related disciplines, including recent leadership in AI-enabled analytics.
- Consumer packaged goods, retail, foodservice, customer, commercial, or RGM experience.
- Demonstrated experience building, coaching, and developing high-performing multidisciplinary teams that include data scientists, data engineers, ML engineers, analytical engineers, BI developers, analysts, and product owners.
- Extensive hands-on experience building analytical models, machine-learning solutions, visualizations, applications, APIs, and products that enable high-impact decisions, with sound judgment in selecting the right methods for each problem.
- Experience leading concurrent functional, internal, and vendor initiatives from concept through deployment, adoption, monitoring, and operational support.
- Demonstrated record of earning the trust of senior leaders through judgment, candor, technical credibility, and consistent delivery.
Proffered Qualifications
Enterprise Data Foundation and Data Modeling
- Expert-level understanding of enterprise and external datasets, including source systems, grain, keys, relationships, hierarchies, measures, lineage, quality, security, governance, and fitness for use.
- Deep expertise in designing dimensional, relational, semantic, analytical, and feature-oriented models, including fact and dimension structures, conformed entities, and reusable semantic layers that enable enterprise analytics.
- Extensive experience using data from customer, outlet, product, package, channel, bottler, geographic, POS, syndicated, consumer, shopper, marketing, media, commerce, financial, digital, commercial execution, supply chain, and operations.
AI, Agentic Analytics, and Applications
- Experience across forecasting, scenario modeling, causal analysis, experimentation, segmentation, recommendation, optimization, anomaly detection, geospatial analysis, predictive modeling, explainability, and model evaluation.
- Experience with generative and agentic AI, natural language analytics, governed knowledge retrieval, AI coding agents, and AI-enabled workflows.
- Experience building analytical applications using React, TypeScript, APIs, and modern visualization libraries.
- Deep expertise in visual information design, comparison, hierarchy, chart selection, annotation, interaction, narrative flow, and executive storytelling.
- Strong software engineering foundations, including modular design, version control, code review, automated testing, CI/CD, configuration, security, observability, deployment, and production support.
- Experience with Microsoft Fabric, OneLake, Synapse, Azure AI, Power BI, and modern enterprise data solutions.
The Coca-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1-B visa status and other employment-based nonimmigrant visas) for this position. Accordingly, all 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.
Skills:
Pay Range:
United States of America: 202,000 USD - 229,000 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
30
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of America
City/Cities:
Atlanta
Travel Required:
00% - 25%
Relocation Provided:
No
Job Posting End Date:
August 28, 2026
Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.