Staff Ux Researcher, Personalization

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

Staff UX Researcher focused on personalization for Oura's health guidance, driving research for AI/LLM-powered recommendations, trust, and explainability. Will own end-to-end research, design and run rapid experiments, and study how personalization adapts across segments. Requires experience researching AI-driven features and translating complex findings into design principles.

What you'd actually do

  1. Own end-to-end research across generative, evaluative, and strategic studies to understand how members experience personalized biometric insights, including how they build or lose trust when recommendations are probabilistic, sensitive, or hard to verify and how they reconcile algorithmic data against their own felt experience.
  2. Design and run rapid experiments (A/B tests, multi-armed variants, staged rollouts) as a primary research tool for validating personalization changes, not solely as a post-hoc check on design decisions.
  3. Lead research into emerging, ambiguous problem spaces — particularly AI- and LLM-powered recommendation experiences — where established methods may need to be adapted or invented.
  4. Study how personalization should adapt across different member segments, health contexts, and levels of engagement, and where more personalization ceases to help and starts to feel presumptuous or invasive.
  5. Help define what "trustworthy" means operationally for Oura's personalization surfaces — transparency about how a recommendation was generated, appropriate hedging under uncertainty, and clear paths for members to correct or override the system.

Skills

Required

  • 8+ years of professional UX or design research experience
  • translating behavioral or usage data into qualitative research that changed a product roadmap
  • qualitative and quantitative research
  • logs/behavioral data analysis
  • experiment design (A/B testing, statistical significance, sample sizing)
  • Comfort reading behavioral analytics and data science output
  • Experience researching AI-driven or algorithmic features
  • Strong storytelling and stakeholder communication skills
  • Ability to operate independently across multiple concurrent research workstreams
  • Thrive in ambiguity

Nice to have

  • Industry experience in health, fitness, wearables, or other high-trust personalized products
  • Familiarity with validated psychological instruments
  • Prior work researching AI-mediated or algorithmically generated health guidance

What the JD emphasized

  • AI- and LLM-powered recommendation experiences
  • trustworthy
  • explainability
  • rapid experimentation
  • behavioral or usage data into qualitative research that changed a product roadmap
  • experiment design (A/B testing, statistical significance, sample sizing)
  • AI-driven or algorithmic features
  • explainability, trust, and appropriate confidence-setting

Other signals

  • AI/LLM-powered recommendation experiences
  • personalization
  • trustworthy AI
  • explainability
  • rapid experimentation