Senior Product Manager - Retail Platforms

Coca-Cola•Atlanta, GA

12h•Remote

About The Position

Data and intelligence are becoming central to how we identify and unlock transaction growth opportunities in Retail. Our ambition is to help sellers see where the biggest opportunities are, understand what actions are most likely to create value, and learn from what happens in the market, so the next recommendation gets better. Getting there requires more than better analytics or another dashboard. We are building product capabilities that bring together data from across the business, predictive models, commercial context, and frontline execution. The goal is to make increasingly sophisticated intelligence simple and useful for a seller: Where should I focus? What should I do? Why does it matter? And what can we learn from what happens next? Our product organization brings together small, empowered teams across product, engineering, data science, design, and the business. Together, we are building the data and intelligence foundation for Transaction Growth and the experiences that put those capabilities into the hands of sellers. If you're excited about turning complex data into better decisions, working across business and technology, and building products that get smarter through use, we'd love to meet you. About the Role The Sr. Product Manager - Retail Platforms will help shape the data and intelligence capabilities that power Transaction Growth in Retail. You'll sit at the intersection of business, data science, and engineering, connecting the commercial problems we're trying to solve with the data, models, and technology needed to solve them. You'll need to be comfortable moving between those worlds: understanding the needs of a seller, working through a model or data-quality question with a data scientist, and making trade-offs with engineering. This is not primarily a backlog-management role. You'll make real product decisions about how data is brought together and made usable, how models and recommendations are evaluated, how intelligence reaches frontline users, and how we measure whether any of it is actually creating value. A central part of the role is treating data and models as part of the product itself. How do we know a recommendation is good? Did the seller act on it? What happened as a result? What should we learn from that outcome? You'll work with the team to build those learning loops into the product, so recommendations become more relevant and useful over time. Ultimately, the work should make something complicated feel simple: help sellers focus on the right opportunities, take the right actions, and drive measurable transaction growth. You'll be part of a small, empowered product team with the autonomy to discover problems, test ideas, make informed trade-offs, and improve the product through continuous learning.

Requirements

5+ years of experience in product management or a related discipline such as data, analytics, data science, engineering, or strategy.
Experience building or managing data-intensive products, platforms, or capabilities.
Enough technical depth to engage meaningfully in decisions involving data architecture, pipelines, APIs, data models, machine-learning models, data quality, and measurement.
Proven ability to translate business problems into product and technical requirements.
Experience working closely with data scientists and engineers to make product decisions and trade-offs.
Strong analytical and problem-solving skills, including experience using data and experimentation to guide decisions.
Experience defining product outcomes and determining whether a product or capability is creating measurable value.
Ability to operate effectively in ambiguous environments with evolving requirements and incomplete information.
Strong communication skills across technical, commercial, and executive audiences.
Bachelor's degree or equivalent practical experience.
Agile Methodology
Application Development
Business Processes
Business Value Creation
Change Management
Influencing
Microsoft Azure
Microsoft Office
Negotiation
Process Improvement Plans
Risk Mitigation Strategies
Software Development
Software Development Life Cycle (SDLC)
Strategic IT
Vendor Management
Waterfall Model
Data Product Thinking: Treats data, models, APIs, and intelligence as product capabilities, not simply technical dependencies. Thinks about who uses them, what good looks like, and how they create value.
Technical Fluency: Can engage credibly on data architecture, pipelines, APIs, ML models, data quality, and measurement. Doesn't need to write production code, but asks good questions, understands the implications of technical choices, and can make informed product trade-offs.
Commercial & Seller Empathy: Understands how sellers work and the decisions they need to make. Keeps technical complexity behind the scenes and focuses the experience on what is relevant, useful, and actionable.
Model & Recommendation Thinking: Understands that generating a recommendation and generating a good recommendation are different things. Connects model performance to relevance, user behavior, and business outcomes.
Measurement & Learning: Builds measurement into the product from the beginning. Connects recommendations to actions and outcomes, learns from those signals, and uses what the team learns to improve the product.
Strategic & Systems Thinking: Sees the whole system, from business problem and user behavior through data, models, technology, frontline execution, and measurement. Understands how decisions in one part of the system affect the others.
Influence & Communication: Makes complicated topics understandable. Builds common ground among commercial and technical teams and creates clarity when different groups come at a problem from different perspectives.
Execution & Learning: Balances longer-term product foundations with immediate business opportunities. Makes progress without needing every answer up front and adjusts quickly as the team learns.

Nice To Haves

Experience owning data products, recommendation systems, decision-support products, or AI/ML-enabled products.
Working knowledge of modern data architectures and how data moves from source systems through pipelines, services, and models into user-facing products.
Experience partnering with data science teams on model development, validation, deployment, and improvement.
Experience designing feedback loops in which user behavior and downstream outcomes inform future recommendations.
Experience with experimentation, model evaluation, or other methods for understanding whether recommendations are creating incremental value.
Experience building products for frontline sales, sales enablement, retail execution, or other field-based users.
Experience simplifying sophisticated analytics or intelligence into intuitive experiences for nontechnical users.
Experience balancing longer-term platform investments with immediate product and business needs.
Advanced degree in business, computer science, engineering, data science, analytics, or a related field is a plus.

Responsibilities

Own the vision, outcomes, and roadmap for Retail Transaction Growth data and intelligence capabilities.
Define the business and user problems the team should solve and establish measurable outcomes for success.
Connect Retail priorities to the data, models, and technical capabilities needed to support them.
Balance foundational investments in data and technology with near-term opportunities to create value for sellers.
Use evidence from the market to continually reassess priorities and where the team should invest.
Treat data, models, and decisioning capabilities as products, with clear users, outcomes, quality expectations, and measures of success.
Work with engineering to understand how data is sourced, transformed, connected, and made available to products, and make informed trade-offs around architecture, pipelines, APIs, quality, and reliability.
Partner with data science to define what makes a recommendation or prediction useful and how model performance should be evaluated.
Determine what signals we need to understand whether recommendations are relevant, whether sellers act on them, and what happens as a result.
Build feedback loops that allow market behavior and outcomes to improve future recommendations.
Spend time with sellers and commercial teams to understand how they work, the decisions they make, and where better data or intelligence could help.
Lead discovery through field research, data analysis, experimentation, prototyping, and testing.
Translate what we learn into clear priorities and product requirements.
Partner closely with engineering, data science, and design to build solutions that are valuable, usable, feasible, and viable.
Define measurement up front and use results to decide what to improve, scale, change, or stop.
Make thoughtful trade-offs when evidence is incomplete and priorities compete.
Turn complex data and model outputs into guidance that sellers can easily understand and act on.
Build a deep understanding of seller workflows and ensure recommendations fit naturally into how work gets done.
Help sellers understand where to focus, what action to take, and why it matters without requiring them to understand the complexity behind the recommendation.
Use seller behavior and market outcomes to understand where the product is working and where it needs to improve.
Move comfortably between commercial, product, data science, engineering, and design conversations.
Translate business problems into terms technical teams can act on and technical constraints into choices business partners can understand.
Communicate product vision, priorities, decisions, and trade-offs clearly.
Build alignment across teams without relying on formal authority.
Help create a culture that values evidence, experimentation, learning, and measurable outcomes.