Principal Product Manager - Tech, Personalization and Monetization, Prime Video Personalization & Discovery

Amazon Amazon · Big Tech · Seattle, WA · Project/Program/Product Management--Technical

Principal Product Manager for Prime Video's Personalization and Monetization, focusing on ML-powered features, recommendation systems, and customer experience. This role involves defining product strategy, translating business problems into algorithmic requirements, and guiding the ML lifecycle to ship scalable, customer-centric AI features.

What you'd actually do

  1. Own the roadmap and strategy for personalization products, including recommendation systems and ML/science-based 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, track, 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

  • 8+ years of technical product management experience
  • 6+ years of end to end product delivery experience
  • Experience with feature delivery and tradeoffs of a product
  • Experience owning/driving roadmap strategy and definition
  • Experience leading engineering discussions around technology decisions and strategy related to a product
  • Experience managing consumer-facing products where ML directly impacts user experience and engagement.
  • Experience leading science and technology discussions and influencing strategy for ML- or science-driven products.
  • 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

  • Experience in content discovery, e-commerce, search, or recommendation systems
  • Knowledge of modern ML techniques (deep learning, NLP, LLM, Agentic AIs) and their practical trade-offs.
  • Strong leadership skills with ability to influence cross-functional teams
  • Experience working with senior-level stakeholders
  • Experience in project management methodologies, business analysis, or process improvement

What the JD emphasized

  • ML-powered features at scale
  • recommendation systems
  • ML/science-based models
  • algorithmic objectives
  • model lifecycle
  • customer value
  • measurable business outcomes
  • complex algorithmic capabilities

Other signals

  • ML-powered features at scale
  • recommendation systems
  • ML/science-based models
  • algorithmic objectives
  • model lifecycle