Senior Manager, Data Science and Agentic AI

The Coca-Cola Company is evolving into a more digital-first, data-driven enterprise to fuel sustainable growth, increase speed to market, and unlock new sources of value. Rapid advances in data and AI create a unique opportunity to accelerate the KO system’s next chapter of growth. In LATAM, this ambition takes shape through our data vision for the 2030 Long-Range Plan: connecting our data assets across Consumer, Customer and Enterprise into one trusted, AI-ready intelligence ecosystem, and running the business on connected intelligence rather than isolated reports. 

This next phase moves us from early wins to scaled, end-to-end impact — powered by a growth engine that embeds data science and agentic AI into our most critical business domains. Success demands more than technology: it requires a product-centric operating model and a culture of experimentation, data fluency, and seamless partnership between business and digital teams. 

Role Overview 

The Coca-Cola Company is seeking a Senior Manager, Data Science and Agentic AI to design, build and scale the models and AI agents that turn LATAM’s data into decisions. Sitting within the LATAM Data & Analytics team, you will take priority use cases from experimentation to production — combining classic data science (predictive/prescriptive modeling) with modern agentic AI (LLM-powered assistants and multi-step agents) on top of our governed data foundations. 

You will build and operationalize AI agents such as data assistants, grounding them in trusted data and business logic so they reason reliably and safely. Working on the target stack (Microsoft Fabric/OneLake, Azure AI Foundry, Azure ML, and our in-house AI platform), you will partner with data engineering, product and governance teams to ensure every model and agent is AI-ready, monitored and cost-controlled. 

This is a hands-on senior individual-contributor: deep enough to build, and senior enough to shape how we design, evaluate and scale agentic solutions across the OU. 

What You’ll Do for Us 

Data Science & Modeling 

• Build predictive and prescriptive models (forecasting, elasticity, segmentation, next-best-action) that support revenue growth, commercial execution and enterprise decisions. 

• Take use cases from PoC to production with sound experimentation design, feature engineering, validation, and clear measurement of business impact. 

• Analyze large, complex datasets to surface trends, drivers, risks and optimization opportunities. 

Agentic AI (core) 

• Design and build AI agents and copilots — LLM-powered assistants and multi-step/agentic workflows grounded in governed data and business logic. 

• Apply modern GenAI patterns — RAG, prompt and context engineering, tool/function calling, orchestration and multi-agent design, with semantic/ontology grounding so agents reason over meaning, not raw tables. 

• Operationalize agents on the target stack — Azure AI Foundry + Microsoft Fabric (with Azure ML and the Nexus AI platform), including deployment, versioning and integration into business workflows. 

• Evaluate and harden agents for production — build eval sets and guardrails; monitor accuracy, hallucination, bias and model/agent drift; track token cost against per-use-case budgets. 

• Embed Responsible AI and governance — align with the AI-Ready Dataset guidelines and T1/T2/T3 scoring from the AI LATAM Office, ensuring lineage, auditability and safe, compliant use. 

Collaboration, MLOps & Adoption 

• Partner cross-functionally with Commercial, Marketing, Finance, Supply Chain and Strategy to frame problems and land solutions that change decisions. 

• Work with data engineering and governance to strengthen data accessibility, quality and AI-readiness, and to apply MLOps practices (CI/CD, monitoring, reproducibility). 

• Translate technical results into business stories and clear recommendations for senior stakeholders. 

• Champion data & AI fluency — coach analysts, share reusable patterns, and promote experimentation and evidence-based decision-making. 

Requirements & Qualifications 

• Bachelor’s degree required in Data Science, Statistics, Computer Science, Engineering, Analytics or related field (Master’s a plus). 

• 5+ years of experience in data science, machine learning or advanced analytics, including hands-on delivery of models into production. 

• Practical experience building GenAI/agentic solutions — LLM applications, RAG, prompt/context engineering, tool calling and agent orchestration (LangChain, Semantic Kernel, Azure AI Foundry or similar). 

• Strong programming and ML foundations — Python (pandas, scikit-learn, and a modern deep-learning/LLM framework), SQL, and solid statistics/experimentation. 

• Experience on cloud data & AI platforms — Azure preferred (Azure ML, Azure AI Foundry/OpenAI), plus Microsoft Fabric/Databricks; familiarity with MLOps and vector stores. 

• Awareness of Responsible AI and governance — evaluation, guardrails, bias/drift monitoring, lineage and cost control; awareness of EU AI Act and NIST AI RMF as reference frameworks is a plus. 

• Strong communication and business acumen — able to explain complex models and agents to technical and non-technical audiences and connect them to measurable outcomes. 

• Collaboration in matrixed, global teams with the ability to manage multiple priorities in a fast-paced environment. 

• Familiarity with the Beverage/System landscape (RGM, Commercial, Revenue) is a plus.