84.51° is a retail data science, insights, and media company that helps brands grow by delivering smarter, more accountable marketing powered by Kroger’s first-party data. Kroger Precision Marketing (KPM) is our commercial arm, enabling brands to connect with customers through insights, incentives, and media.

Role Summary

Kroger Precision Marketing (KPM) recently launched a new AI Platform team to help product teams bring safe, scalable AI to market across our brand-facing platforms. We do that by (1) consulting on standards and guardrails; (2) accelerating reusable tools and early features, then transitioning long-term ownership to product teams; and (3) operationalizing a repeatable way to ship, monitor, and improve AI at scale.

As a Product Manager on this team, you will work alongside other PMs and a Product Director who sets the overall vision and strategy. You will own end-to-end initiatives that span multiple projects and features, from discovery through delivery and adoption, and you will be accountable for the business outcomes and KPIs those initiatives drive. You will also build and evolve reusable AI tooling for other teams, and consult with those teams to align strategies, reduce duplication, and ensure we ship AI consistently and safely across KPM’s brand-facing platforms.

Key Responsibilities

• Build the game plan: translate the AI portfolio vision into a clear roadmap for your initiatives
• Pick the right bets: focus the roadmap on the highest-impact work by weighing impact, effort, and risk, and deprioritizing the rest
• Stay ahead of the market: stay current on AI trends, vendors, and competitor moves, and use those insights to inform recommendations and influence our strategy
• Have a point of view, stay curious: form a clear perspective, seek input early, and adjust quickly when evidence points to a better approach
• Get close to users: partner with Experience Design to run discovery and define what we will build, why it matters, and why now
• Ship with momentum: drive delivery with Agile Delivery, Data Science, and Engineering by managing scope, dependencies, risks, tradeoffs, and timelines
• Prove and improve: measure outcomes against business KPIs, collect user and stakeholder feedback, and iterate based on real signal (qualitative and quantitative)
• Make it stick: support rollout and adoption, then hand off with clear enablement and accountability
• Scale the capabilities: own reusable AI capabilities, including skills, patterns, and tooling, that help teams across the company build smarter and faster
• Tell the story: communicate progress, tradeoffs, and decisions with clarity, and raise risks early with context and options
• Be the go-to partner: earn trust through strong follow-through, clear communication, and reliable decision-making, while balancing confidence with humility

Minimum Qualifications

• Product management: 3–5 years of product management experience
• AI/ML delivery: familiarity with the AI/ML product lifecycle, including data readiness, model development, deployment, monitoring, and governance
• GenAI fundamentals: working knowledge of prompting, retrieval-augmented generation (RAG), agent patterns and skills, and quality and safety evaluation
• Roadmap: ability to set priorities and drive execution independently
• Decision-making: ability to make calls, clarify tradeoffs, and create clarity in ambiguity
• Delivery: experience partnering with Experience Design, Data Science, Engineering, and Agile Delivery
• Communication: ability to translate between technical and business teams, manage stakeholders, and raise risks early with context and options
• Metrics: ability to define success, measure outcomes, and iterate based on real signal
• Leadership: a humble, direct leadership style, comfortable saying “I don’t know,” and confident leading toward an answer
• Collaboration: ability to move fast within security, governance, and complex stakeholder environments

Preferred Qualifications

• B2B platform: experience building products for large, diverse internal or external user bases
• Industry: familiarity with retail analytics, retail media, adtech, and data-driven marketing platforms
• Technical fluency: background in engineering, data science, analytics, or a highly technical product area, with the ability to go deep on architecture and delivery tradeoffs
• Experimentation and measurement: product analytics experience defining success metrics, selecting the right measures for the job (for example, using Google’s HEART framework), and running experiments to quantify user and business impact

#LI-EB1