Senior Product Manager - Tech, Prime Video Personalization & Discovery

Amazon Amazon · Big Tech · Culver City, CA · Project/Program/Product Management--Technical

Senior Product Manager - Tech for Prime Video Personalization & Discovery, focusing on owning the vision, strategy, and roadmap for core recommendations powered by ML models. The role involves collaborating with scientists and engineers, defining metrics, leading A/B testing, and making trade-off decisions for production-ready solutions.

What you'd actually do

  1. Own the strategy and roadmap for personalization products, including recommendation systems and ML models
  2. Collaborate with applied scientists and ML engineers to translate business problems into model requirements and convert them into clear algorithmic objectives, metrics, and guardrails.
  3. Define and analyze key metrics to measure recommendation quality, customer outcomes, and business impact; analyze user behavior data to identify opportunities to improve personalization.
  4. Lead A/B testing strategy and experimentation to validate algorithmic improvements and inform roadmap decisions.
  5. Partner with stakeholder teams to understand business needs and translate them into technical specifications and prioritized work.

Skills

Required

  • 5+ years of technical product or program management experience
  • 3+ years of end to end product delivery experience
  • Bachelor's degree
  • Experience contributing to engineering discussions around technology decisions and strategy related to a product
  • Experience with feature delivery and tradeoffs of a product
  • Experience owning/driving roadmap strategy and definition
  • Experience managing technical products or online services
  • Experience in representing and advocating for a variety of critical customers and stakeholders during executive-level prioritization and planning
  • Background in an applied science, engineering, or quantitative field, with a solid understanding of the model lifecycle, data readiness, and model evaluation frameworks

Nice to have

  • Knowledge of machine learning approaches and algorithms
  • Experience in building and driving adoption of new tools
  • Experience influencing multiple stakeholders and leading cross functional teams across geographies and business units

What the JD emphasized

  • customer value
  • measurable business outcomes
  • model requirements
  • algorithmic objectives
  • metrics
  • guardrails
  • customer outcomes
  • business impact
  • algorithmic improvements
  • technical specifications
  • model complexity
  • latency
  • scalability
  • business value
  • production-ready solutions
  • applied science
  • engineering
  • quantitative field
  • model lifecycle
  • data readiness
  • model evaluation frameworks

Other signals

  • ML-powered features at scale
  • recommendation systems
  • ML models
  • customer value
  • business outcomes