RGM Product Manager

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 operating model 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, RGM, you will own the what and the why of how AI shows up in the Global RGM product: which commercial decisions agents should support, for whom, in what order, and how success is measured in commercial impact, adoption, and responsible delivery. You will work within a persistent, cross-functional product team alongside Data Science, Engineering, UX, Architecture, and RGM experts. Engineering owns how the product is built; you own everything that determines whether it is worth building.

Product direction starts in market. You will spend time with RGM teams at operating units and bottlers to understand how pricing, promotion, and portfolio decisions are actually made, where current ways of working break down, and where AI can credibly change the outcome. You will convert that discovery into direction: clear problem statements, an 18-month AI product roadmap, and requirements that tell the team not just what to build, but why it matters commercially.

Day to day, this is a hands-on AI product seat. You will write user stories, acceptance criteria, and evaluation criteria for agent behavior, prioritize the backlog against commercial value, define what "good" looks like for each release (including the quality bar for AI outputs), and sign off that what shipped does what the business needed. It demands deep technical judgment across GenAI, retrieval-augmented generation, model context protocol, agent patterns, and evaluation, paired with genuine fluency in what the product produces (elasticities, uplift against baseline, share math) and in the commercial world those recommendations land in. You will stay engaged across the full product lifecycle, from discovery and pilot design through launch, adoption measurement, and continuous improvement - because adoption is the truest test of whether you defined the right product.

The role is global across a commercial system that spans more than 200 countries and territories, roughly 200 bottling partners, and 30 million retail customer outlets - few products anywhere offer a larger canvas. It is also a first seat: the AI product discipline you establish here becomes the pattern for how agentic RGM scales to every market.

What You Will Do for Us
Set the AI product vision: define how AI shows up across the Global RGM product, anchored in commercial value for operating units and bottlers, responsible delivery, and long-term platform leverage.

Own the AI product roadmap: define the problems worth solving, sequence agent, model, and data capabilities by commercial value into a clear 18-month roadmap, and present the roadmap and the value delivered to OU leadership and bottler executives.

Run continuous discovery with users: sit with RGM managers and walk a run end to end (which price moves to take, which to skip, and why) to understand where the product creates value, where it falls short, and where AI can credibly change the outcome.

Translate discovery into AI requirements: write problem statements, user stories, and acceptance criteria the team can build against, including how agents should behave, which data and tools they may use, and when they should decline or escalate to a person.

Own and prioritize the backlog against commercial value, with the judgment to say no, sequence competing requests, and defend a prioritization call to senior stakeholders.

Define outcomes and success measures: set product, AI quality, adoption, and commercial impact metrics for each release, and give business sign-off that shipped functionality does what the market actually needed.

Make evaluation a product discipline: define evaluation sets and quality thresholds for agent outputs and model recommendations, combining deterministic checks with evaluation of non-deterministic outputs, and partner with the AI engineer to rate generated insights so recommendations that would not survive scrutiny in front of a bottler are caught before the bottler sees them.

Design for governance: make RGM rules of engagement (price floors and ceilings, discount depth and promotion frequency, and price-gap and price-index thresholds) and responsible AI guardrails capabilities the product enforces rather than policies applied around it.

Embed the closed-loop RGM workflow: build agents into each step (insight generation and opportunity sizing, strategy setting, guardrail definition, in-market execution, and post-implementation evaluation) so markets operate a consistent, repeatable process.

Shape reusable capabilities: identify and define shared agent skills, retrieval patterns, MCP tool integrations, and evaluation methods that reduce duplication and let the product scale across markets.

Align technical direction: partner with Engineering, Data Science, and Architecture to evaluate technical options (model choice, retrieval design, agent orchestration, build versus buy) and make product decisions based on risk, cost, scalability, and platform fit.

Shape go-to-market: partner with Commercial, RGM, and Strategy teams so product strategy informs how capabilities roll out to operating units and bottlers, while market needs and commercial goals shape roadmap decisions.

Coordinate across product teams: partner with adjacent product teams to align roadmaps, clarify ownership, and manage dependencies so connected capabilities come together cohesively.

Stay ahead of the market: track emerging AI capabilities, CPG and RGM innovation, competitive moves, and vendor offerings, and translate that signal into a differentiated position Coca-Cola can credibly pursue.

Make smart tradeoffs: build the business case for significant capability requests, balancing user needs, commercial value, technical readiness, risk, cost, and reuse potential to decide what to accelerate, pause, partner on, or stop.

Drive adoption and impact: lead each release through launch planning, test-and-learn pilots (hypothesis framing, control groups, incrementality measurement), and short playbooks (how to read an elasticity, when to trust a recommendation), then track adoption honestly per market and feed what stalls back into the backlog.

Tell the product story: partner with the UX designer on launch communication and demo material, and communicate progress, risks, tradeoffs, and decisions clearly to executives, OU and bottler leadership, and the delivery team.

Raise the bar for AI product practice: share standards, templates, and lessons that help other teams build AI quickly, consistently, and safely, leading through influence rather than authority.

Requirements and Qualifications
Product experience: 8+ years of product management experience, including taking AI/ML, GenAI, data, or analytics products from discovery through launch and adoption.

Education: Bachelor's degree in Business, Economics, Computer Science, Engineering, or a related field, or equivalent experience; MBA or technical postgraduate degree preferred.

AI expertise: deep understanding of GenAI, AI/ML product development, retrieval-augmented generation (RAG), model context protocol (MCP), agent patterns and skills, agent-to-agent interactions, data readiness, model development, evaluation, deployment, monitoring, governance, responsible AI practices, quality controls, and safety guardrails, with the technical judgment to shape product direction, weigh architecture tradeoffs, and challenge assumptions with Engineering and Data Science.

AI product craft: experience writing requirements for non-deterministic systems, defining evaluation sets and quality thresholds, specifying agent behavior and escalation, and monitoring AI quality after launch.

Product strategy: ability to set long-term direction for a product, build a coherent 18-month roadmap, and make tradeoffs across competing priorities, including the judgment to say no and defend a prioritization call to senior stakeholders.

RGM fluency: strong understanding of the core RGM levers (headline pricing, price-pack architecture, trade promotion optimization, assortment, mix, and trade terms) and how bottlers go to market, with the ability to read elasticities, uplift against baseline, and share math and explain their limits as clearly as their conclusions.

Commercial partnership: proven ability to work with Commercial and Strategy leaders so product strategy informs go-to-market, and market needs, customer priorities, and commercial goals shape product direction.

Technical partnership: deep experience working with Engineering, Data Science, and Architecture to define product requirements, evaluate architecture options, and make decisions based on risks, tradeoffs, scalability, and long-term platform fit.

Influence without authority: experience aligning stakeholders and adjacent product teams in a matrixed, franchise environment, coordinating dependencies, clarifying ownership, and embedding new ways of working.

Product judgment and measurement: strong product intuition combined with evidence from product analytics, user research, customer feedback, and experimentation, including pilot design with control groups and incrementality measurement.

Systems thinking: ability to map workflows end to end and design reusable product patterns and governance mechanisms that help teams build AI faster, with more consistency and trust.

Clear communication: ability to write short, clear one-pagers and translate complex product, technical, and commercial topics into recommendations, risks, and decisions for audiences from analysts to executives.

Market awareness: ability to translate AI trends, competitive moves, and emerging capabilities into a differentiated strategy for Global RGM.

Hands-on AI fluency: practical, daily use of LLM-based and agentic tools for real work, with a clear view of where they fail, plus enough SQL to pull and check your own numbers.

Preferred, platform experience: B2B or enterprise platform products that serve multiple product teams, business users, or customer groups.

Preferred, industry depth: RGM, pricing, or commercial experience at a CPG company, bottler, or retailer, or in revenue management engagements at a top-tier consulting firm.

Preferred, technical background: direct experience in Engineering, Data Science, Architecture, or another technical discipline, which strengthens credibility and decision quality on complex technical choices.

A global role supporting operating units and bottlers in every region requires working across time zones, including early and late calls, availability outside standard business hours, and travel to markets.