Product Manager, Marketplace Productivity Solution

Amazon Amazon · Big Tech · 13, Japan +1 · Project/Program/Product Management--Non-Tech

Product Manager for AI-powered productivity solutions in Amazon's Japanese marketplace. Responsibilities include understanding user needs, defining requirements, driving delivery with engineering and data science, and measuring impact. The role focuses on leveraging generative AI to automate processes, surface insights, and enable faster decision-making. Success requires evaluating AI output quality, designing evaluation frameworks, and iterating based on data.

What you'd actually do

  1. Identify and validate critical user needs through direct conversations, workflow observation, and data analysis — not assumptions.
  2. Translate validated needs into prioritized requirements (P0/P1/P2) with clear acceptance criteria for engineering and data science teams.
  3. Own milestone-based delivery: requirements aligned → goals set → roadmap locked → launch criteria defined → testing complete → launch → impact measured.
  4. Define measurable evaluation metrics for AI-powered features — accuracy thresholds, error rates, user satisfaction baselines — as part of launch criteria, not as an afterthought.
  5. Design and maintain evaluation frameworks to continuously monitor and improve AI output quality post-launch.

Skills

Required

  • 3+ years of product management or equivalent (PdM, technical project lead) driving delivery with engineering teams
  • Experience writing business requirements and translating complex workflows into prioritized specifications
  • Comfort with SQL and Python for data analysis and process automation
  • 3+ years conducting user research, workflow analysis, and requirements gathering
  • Strong analytical skills — ROI models, impact quantification, success metrics definition
  • Effective communicator across technical and non-technical audiences
  • Business-level Japanese (N1/native) and English

Nice to have

  • Experience with AI/ML-powered products — understanding what LLMs can do, and how to evaluate AI output quality
  • Experience designing evaluation frameworks for AI features (accuracy metrics, automated test datasets, quality monitoring)
  • Experience redesigning operational processes at scale — replacing manual workflows with automated solutions
  • Experience modernizing legacy systems (e.g., replacing Excel/VBA-based processes with scalable, systematic approaches)
  • Background in e-commerce, marketplace, or sales operations

What the JD emphasized

  • end-to-end product outcome
  • measuring real-world impact
  • measuring real productivity gains
  • continuously improving AI quality based on data
  • Define measurable evaluation metrics for AI-powered features
  • Design and maintain evaluation frameworks to continuously monitor and improve AI output quality post-launch

Other signals

  • Product Manager
  • AI-powered productivity solutions
  • generative AI
  • evaluate AI output quality
  • design evaluation frameworks
  • measure real productivity gains
  • continuously improving AI quality