Senior Product Manager, Catalog

The Instacart Catalog team is the product data infrastructure powering everything customers see, find, and trust across the marketplace. We ingest data from retailers and third-party sources and use AI/ML and human operations to validate, enrich, and publish product information at scale. At the heart of this infrastructure are product attributes — the structured descriptors that power search filters, item card badges, dietary safety signals, health personalization, brand advertising, and agentic commerce. They touch every major surface and every major stakeholder.

We're looking for a Senior Product Manager to own the product data layer that determines how customers find what they need, how brands show up on shelf, and how Instacart earns trust as the definitive source of grocery product data. In this role, you'll set the end-to-end strategy for catalog attributes, lead governance, and drive accuracy and coverage at scale — building the kind of trust that brands, retailers, and internal teams depend on.

You'll work alongside a team of 7 engineers and partner closely with Machine Learning and Operations, as part of a broader product org of 4 PMs on the Catalog team. You'll thrive here if you love operating at the intersection of ML/AI, operations, and commercial partner relationships — and can hold a complex, multi-sided system together with clear thinking and strong execution.

• Own the end-to-end strategy and roadmap for catalog attributes — aligning cross-functional partners around clear outcomes, success metrics, and milestones, and maintaining a roadmap process transparent enough that every side of the marketplace can depend on it.
• Translate ambiguous, complex problems into structured plans and drive execution from discovery and prioritization through launch, iteration, and scale, spanning a development process that includes Ops, Engineering, ML, Legal, and commercial partners.
• Use data and customer insights to run experiments, understand root causes, and design solutions that balance speed and accuracy across the attribute lifecycle — defining KPIs, interpreting results, and making sound tradeoffs between precision and recall.
• Build strong relationships and feedback loops across Product, Engineering, Design, Analytics, Operations, Machine Learning, Sales, Ads, Search, Legal, Brand, and Support — communicating transparently to drive clarity, commitment, and shared ownership of attribute quality.
• Establish and continuously improve mechanisms for operational excellence — owning governance rituals, quality dashboards, postmortems, and platform investments that make attribute quality defensible at scale and compound over time.

Minimum Qualifications

• 5+ years of product management experience, with meaningful time spent in catalog, data platforms, content systems, or ML/AI-adjacent products — including ownership of complex, cross-functional programs with measurable impact.
• Proven ability to set strategy, prioritize ruthlessly, and deliver high-quality outcomes in fast-paced, dynamic environments — including building governance structures and quality systems, not just shipping features.
• Demonstrated proficiency using data to drive decisions — defining KPIs, partnering with Analytics, interpreting experiments, and reasoning about precision/recall and ML model tradeoffs — with comfort navigating ambiguity to find clarity.
• Exceptional written and verbal communication skills, with a track record of building trust and alignment across high-stakeholder-count environments where no single team controls all the inputs.

Preferred Qualifications

• Experience in marketplaces, logistics, e-commerce, or consumer technology, with familiarity balancing multi-sided incentives.
• Hands-on experience with experimentation and analytics — including A/B testing, SQL, and dashboards — and the ability to partner deeply with Engineering and Data teams.
• Experience with product attribute systems, catalog data, content management, or source prioritization at marketplace scale.
• Familiarity with data pipelines, ML extraction workflows, or quality evaluation frameworks such as golden set audits, human-in-the-loop review, or precision/recall measurement.
• History of translating commercial commitments into engineering requirements, or prior work at the intersection of legal/compliance and data products — including rights management, regulatory compliance attributes, or data sourcing constraints.
• People leadership experience — direct or cross-functional — with a genuine passion for coaching, feedback, and fostering inclusive team culture.

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