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) has launched an AI Platform domain to define how AI is built, scaled, and used across internal teams and external brand-facing platforms. This is a high-impact opportunity to shape how KPM does AI: setting the vision, strategy, and operating model that help product teams turn emerging technology into practical, trusted, production-ready capabilities.

The AI Platform domain brings that strategy to life by consulting with teams on shared resources, standards, guardrails, best practices, and upskilling; accelerating innovation by building early capabilities on behalf of teams before transitioning long-term ownership; and operationalizing reusable skills, tools, access layers, and delivery patterns that reduce the path from idea to production-ready launch. This leader will have the trust and autonomy to shape strategy, take thoughtful creative bets, iterate quickly, and build the operating model needed to make AI a durable advantage for KPM.

Key Responsibilities

• Set the AI vision: define the product vision and strategy for how AI shows up across KPM, anchored in customer value, commercial impact, responsible delivery, and long-term platform leverage
• Lead the domain portfolio: manage multiple product teams across the AI Platform domain, ensuring roadmaps ladder to shared outcomes, clear sequencing, and the highest-value bets
• Coach product leaders: develop Product Managers by helping them shape team vision, set 18-month roadmaps, clarify outcome metrics, and make stronger prioritization and tradeoff decisions
• Define domain outcomes: establish measurable outcomes that Product, Engineering, Design, Data Science, Commercial, and Strategy teams can align around and use to guide planning and execution
• Build the operating system: create repeatable systems, standards, and decision frameworks that help KPM teams develop AI products quickly, consistently, and safely
• Scale reusable capabilities: guide investment in shared AI capabilities, skills, patterns, evaluation methods, and tooling that reduce duplication and accelerate product teams across the business
• Shape go-to-market strategy: partner with Commercial and Strategy leaders to ensure product strategy informs go-to-market motions, while market needs, customer priorities, and commercial goals shape roadmap decisions
• Align technical direction: partner with Engineering, Data Science, and Architecture leaders to define requirements, evaluate technical options, and make decisions based on risks, tradeoffs, opportunities, scalability, and platform fit
• Coordinate across product domains: partner with peer Product Directors to align roadmaps, clarify ownership, manage dependencies, and ensure broader KPM outcomes are pursued cohesively across domains
• Stay ahead of the market: track emerging AI strategy, retail media innovation, competitive moves, and vendor capabilities, then translate that signal into a differentiated niche KPM can credibly pursue
• Make smart portfolio tradeoffs: balance customer needs, business value, technical readiness, risk, cost, and reuse potential to decide what to accelerate, pause, partner on, or stop
• Drive adoption and impact: build a productionized AI engine by shipping new capabilities, iterating on released features, defining rollout plans, establishing success measures, and creating feedback loops that continuously improve quality, adoption, and business impact
• Tell the strategic story: communicate the AI portfolio narrative, progress, risks, tradeoffs, and decisions with clarity for executives, cross-functional leaders, and delivery teams
• Build a high-trust culture: create an environment where teams move fast, learn quickly, challenge assumptions, and stay accountable to shared outcomes

Minimum Qualifications

• Product leadership: 6-10+ years of product management experience, including experience leading multiple product teams, domains, or complex product portfolios

• Degree Requirements: Bachelor’s degree or equivalent experience.

• Portfolio strategy: ability to set long-term direction, connect multiple team roadmaps into a coherent 18-month portfolio plan, and make tradeoffs across competing priorities
• People leadership: experience coaching Product Managers, raising the quality of roadmaps, improving decision-making, and helping teams translate strategy into clear outcomes and execution plans
• 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 vision, guide architecture tradeoffs, and challenge assumptions across Product, Engineering, and Data Science
• Commercial partnership: proven ability to partner with Commercial and Strategy leaders to shape how product strategy informs go-to-market strategy, and how market needs, customer priorities, and commercial goals shape product direction
• Technical partnership: deep experience partnering with Engineering, Data Science, and Architecture leaders to define product requirements, evaluate technical architecture options, and make decisions based on risks, tradeoffs, opportunities, scalability, and long-term platform fit
• Cross-domain alignment: experience partnering with peer Product Directors to coordinate roadmap dependencies, clarify ownership across domains, sequence when roadmaps come together, and ensure broader KPM outcomes are pursued cohesively across connected product areas
• Product judgment and measurement: ability to combine strong product intuition with evidence from product analytics, user research, customer feedback, experimentation, and portfolio-level success metrics, using data to pressure-test decisions, identify what teams should chase next, and build a compelling case for why it matters
• Systems thinking: ability to design scalable operating models, reusable product patterns, and governance mechanisms that help teams build AI faster, with more consistency and trust
• Executive communication: ability to translate complex product, technical, and commercial topics into clear recommendations, risks, decisions, and strategic narratives
• Market awareness: ability to translate AI trends, competitive moves, and emerging capabilities into a differentiated strategy for KPM

Preferred Qualifications

• B2B software experience: experience leading B2B software products, ideally platform products that serve multiple product teams, business users, or enterprise customer groups
• Industry depth: familiarity with retail analytics, retail media, adtech, data-driven marketing platforms, and the commercial models that support them
• Technical background: direct experience in Engineering, Data Science, Architecture, or another technical discipline is preferred, as it strengthens empathy, credibility, and decision quality when working through complex technical choices

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