Senior Director, RGM Product AI Engineering

About the Role

Revenue Growth Management (RGM) is one of The Coca-Cola Company’s most important commercial capabilities, and not simply a pricing discipline. Coca-Cola positions RGM alongside brand building, innovation, and integrated execution as a core capability for topline growth, and our growth strategy names it a key commercial capability for determining where to play and how to win.

RGM connects consumer demand to the economics of the system. It determines what we sell, in which packages, at what price points, through which channels and customers, and with what promotional investment, all in service of balanced growth across transactions, volume, and price/mix. Done well, it finds incremental revenue pools and converts them into commercial action, improves the economics of every price, pack, assortment, and promotion decision, builds the affordability and premiumization ladders that bring more consumers into the portfolio, and gives Coca-Cola and its bottlers a common fact base for growing customer revenue and profit rather than simply negotiating price.

We are now reimagining RGM for the Agentic AI era, and we are building it ourselves. Our Global RGM Product is evolving from a suite of analytical solutions into an intelligent decision platform where AI agents interact directly with enterprise data, invoke advanced analytics and optimization models, run and compare scenarios, and hand commercial teams a recommendation with the reasoning attached. Work that once took an analyst a week (pulling the data, running the model, writing the story, defending the number) is being compressed into a conversation.

Few companies are attempting this at this scale, and fewer are still against decisions this consequential: real pricing, promotion, and portfolio moves, made across markets and bottlers. The work sits at the frontier of the CPG industry, spanning agent orchestration, retrieval over enterprise data, and the evaluation of non-deterministic and deterministic systems, and none of it is a research exercise. What we build ships to commercial teams who use it to make decisions the same week.

This is an opportunity to build, not just design, the next generation of commercial decision intelligence at Coca-Cola, and to establish the technical foundation for agentic RGM at global scale. The patterns set here will shape how one of the world’s largest commercial systems makes its most important decisions. If you want your work in front of real users, at real scale, from the first release, this is that role.

.

Role Overview

As Product Manager of AI Engineering for the Global RGM Product, you will own the intelligence layer of the RGM suite within a persistent, cross-functional product team working alongside Product Management, Data Science, Engineering, UX, Architecture, and RGM experts. This is a Senior Director-level, hands-on engineering role, with technical direction provided by the RGM Product Tech Lead.

RGM turns price-optimization and promotion-simulation runs into decks and insights that RGM teams act on. A pipeline reads the run export, computes the KPIs in code, has the model write the narrative, and a QA step reviews the result before anyone sees it. You will own everything the model touches in that chain: the prompts, the pipeline steps, the retrieval, and the evaluation numbers that say whether an output can be trusted. One house rule sits above the rest—code computes every number, and the model only writes the words.

Working in a product model, you will remain engaged throughout the product lifecycle—from discovery and prototyping new analyses on existing run data through eval-gated release, deployment, measurement, and continuous improvement. The work is judged on evidence: a prompt or pipeline change ships when the quality numbers improve, not when it feels better. The role is global and operates around the clock, supporting markets and teams in every region.

What You Will Do for Us

• Own the prompt behind each pipeline node — validation, narration, slide planning, and QA review — versioned in source control alongside test inputs and expected outputs rather than maintained in a chat window.
• Extend the LangGraph orchestration pipeline in Python: ingest run exports, compute KPIs with pandas, call the model for narrative, and route QA retries when a generated slide fails review.
• Define and track the evaluation metrics for every run — the share of insights analysts rate gold or silver, the share hallucinated or missing a baseline, and volume coverage — and gate prompt changes on those numbers improving.
• Build retrieval over reference material, including methodology documentation, QA references, and past runs, so generated text quotes the underlying data instead of guessing.
• Prototype new analyses on existing run data, from new insight types to competitor-response summaries, promoting a prototype to a feature only when it has a test set and a user who asked for it.
• Integrate the pipeline with the platform alongside the Tech Lead, ensuring every run artifact — prompts, computed KPIs, narrative, and QA verdicts — lands in the SQL Server schema with logging and observability.
• Design human-in-the-loop guardrails so commercial users can see, challenge, and correct what the system generates before it reaches an OU or bottler audience.
• Make and document technical decisions on model providers, orchestration approach, and hosting within enterprise security, privacy, and AI governance requirements.
• Integrate LLM-based capabilities with the traditional machine learning, analytical, and optimization models that produce the underlying pricing and promotion recommendations.
• Design for the system context: multi-market, multi-OU deployment, bottler-facing data boundaries, and role-based access to commercially sensitive pricing and promotion data.
• Partner with the business consultant on insight quality, turning recurring user feedback into concrete pipeline and prompt changes and retesting with the same users once a fix ships.
• Contribute to engineering standards for a codebase with non-deterministic components: testing, observability, and eval-gated releases.

Requirements and Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Technology, or a related technical field; Master’s degree preferred.
• 8+ years of software, data, or ML engineering experience, including deep hands-on Python running in production and operated by other people — services or pipelines rather than notebooks alone — with strong pandas and SQL.
• Demonstrated experience shipping an LLM-enabled feature that real users used, with a clear account of what broke first and how it was caught.
• Treats prompts as code: versioned, tested against a fixed input set, with one variable changed at a time.
• Experience building LLM-enabled systems, including retrieval-augmented generation, text-to-SQL, prompt and agent orchestration frameworks (e.g., LangGraph, LangChain), vector stores, tool/function calling, agentic workflows, and human-in-the-loop guardrails.
• Working knowledge of evaluation practice for generative systems: fixed test sets, rubric-based grading, hallucination and coverage measurement, and eval-gated release.
• Ability to read price, volume, and revenue data by SKU and channel, and to spot claims the numbers do not support.
• Precise written English; the quality of the pipeline’s output is bounded by the quality of its instructions.
• Understanding of how to integrate AI applications with traditional machine learning, analytical, and optimization models.
• Strong understanding of modern software engineering practices, including automated testing, CI/CD, source control, observability, API design, security, and cloud deployment.
• Working knowledge of the Azure data and AI stack, including Azure Data Lake Storage, Databricks, Synapse, Azure Data Factory, MLflow, and Azure OpenAI Service, plus modern application patterns such as API-first middleware (FastAPI, Azure App Service, Azure API Management), Azure SQL, Key Vault, Azure Active Directory/Entra ID, Azure Monitor, and Azure DevOps CI/CD.
• Familiarity with Revenue Growth Management concepts across pricing, promotion, assortment, and mix, and with the analytics behind them — elasticity modeling, optimization algorithms, and forecasting; preferred.
• Exposure to CPG or bottler commercial data, including how pricing and promotion decisions cascade to execution at the point of sale; preferred.
• Sufficient C# to read and reason about the .NET service layer the pipeline integrates with; preferred.
• Strong communication skills, with the ability to explain model behavior, its limits, and its evidence to commercial audiences in business-relevant language.
• Comfort operating in a matrixed, multi-market franchise environment where adoption depends on trust in the output.
• A global role supporting operating units and bottlers in every region requires working across time zones, including early and late calls and availability outside standard business hours when markets or releases de