Staff Product Manager - Member Understanding & Intelligence

Oura Oura · Consumer · San Francisco, CA +1 · Product

Staff Product Manager for Oura's Member Understanding & Intelligence team, focusing on AI/ML capabilities to drive personalization. The role involves shaping strategy, owning roadmaps for platform and member experience layers, understanding member needs, partnering with leadership, driving internal adoption, and measuring impact through experimentation. Requires experience shipping ML-powered personalization or LLM features, prompt engineering, RAG, and managing AI at scale.

What you'd actually do

  1. Shape the vision and strategy for member understanding & intelligence: Define and evolve a product vision for how Oura understands members and serves members truly personalized experiences. Build conviction around which context types unlock the most member value and business impact, balancing platform capabilities with member-facing experiences.
  2. Own outcomes across both platform and member experience layers: Drive a roadmap that balances robust platform capabilities (APIs, data models) with delightful member-facing features. Navigate the tension between building infrastructure that enables other teams and shipping features that prove member value.
  3. Deeply understand our members and their health journeys: Partner with research, science, and data teams to understand which contextual factors actually drive personalization quality and health outcomes. Map end-to-end journeys, identify where generic guidance fails members, and prioritize the context types that matter most for our target ICPs and health outcomes.
  4. Partner with senior and executive leaders: Structure clear narratives and options for high-impact decisions in a high-ambiguity environment. Connect work to company-level goals and use measurable outcomes to build trust and alignment across Oura.
  5. Drive internal adoption of capabilities: Work closely with other product teams to identify the highest-value integration points. Ensure the platform is well-documented, understood, and reliably supports their roadmaps.
  6. Measure impact and learn fast: Define success metrics, instrumentation needs, and experimentation plans for Context Engines. Build the A/B testing infrastructure that enables rapid iteration on personalization approaches. Use data and member feedback to validate which context types drive member value.

Skills

Required

  • 7+ years of Product Management experience in data platform, ML, or consumer technology products
  • Proven track record of shipping successful products at scale
  • At least 2 years at Staff/Principal level or equivalent strategic scope
  • Shipped ML-powered personalization or LLM features in production
  • Hands-on experience with prompt engineering, RAG architectures, and managing cost/performance tradeoffs of AI at scale
  • Understand constraints of shipping GenAI at scale
  • Lead technical roadmap discussions on model integration, experimentation infrastructure, and vendor selection
  • Experience shipping consumer-facing features at scale
  • Ability to design data architectures and ontologies that scale across multiple use cases and teams
  • Comfortable working with concepts like ETL pipelines, data normalization, and interoperability standards

Nice to have

  • prompt engineering
  • RAG architectures
  • managing cost/performance tradeoffs of AI at scale
  • model integration
  • experimentation infrastructure
  • vendor selection
  • consumer product intuition
  • systems thinking
  • ETL pipelines
  • data normalization
  • interoperability standards

What the JD emphasized

  • shipping ML-powered personalization or LLM features in production
  • Hands-on experience with prompt engineering, RAG architectures, and managing cost/performance tradeoffs of AI at scale
  • lead technical roadmap discussions on model integration, experimentation infrastructure, and vendor selection

Other signals

  • driving strategy for how we use member context to deliver truly personalized experiences
  • ship personalization features
  • build foundational capabilities that other product teams leverage
  • lead our US-based AI/ML capabilities
  • shipping ML-powered personalization or LLM features in production
  • Hands-on experience with prompt engineering, RAG architectures, and managing cost/performance tradeoffs of AI at scale
  • lead technical roadmap discussions on model integration, experimentation infrastructure, and vendor selection