Senior Director, AI & Analytical Engineering

About this role

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.

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