Senior Product Manager, Tech, Amazon Manufacturing Services

• Key job responsibilities
• Manufacturing Execution Product Strategy
• Own the 2–3 year product roadmap for the manufacturing execution stack — from scheduling UX through operator tools to AI-powered optimization
• Define the product vision that connects AI-native scheduling intelligence to the physical shop floor experience
• Develop and maintain the execution technology strategy across P0 (manual + semi-automated), P1 (AGV integration, real-time optimization), and P2 (AI-enabled autonomous control)
• Write compelling narratives (PR/FAQs, 6-pagers, OP docs) that articulate product strategy and secure investment from leadership
• Scheduling & Operator Experience
• Serve as the operational product partner to the scheduling engineering team — translating manufacturing floor needs, pain points, and workflows into product requirements
• Own the operator-facing scheduling experience: how assignments surface, how disruptions are communicated, how overrides are captured, and how the system explains its decisions to the right audience
• Drive the scheduling system’s authority progression (Shadow → Advisory → Co-Pilot → Decision Maker) by defining success criteria, measuring override rates, and building supervisor trust through UX design
• Define product requirements for scheduling and explainability tools from the operator/supervisor perspective
• Machine Connectivity & IIoT
• Own the product vision for machine connectivity — defining how equipment telemetry flows from factory floor to data lake to decision systems
• Define integration requirements for manufacturing equipment onboarded at the manufacturing facility (industrial lasers, robotic welding cells, automated coating lines, autonomous mobile robots)
• Develop product requirements for digital twin capabilities — real-time equipment state, simulation for what-if analysis, and predictive modeling
• Partner with automation engineers to define the machine-to-cloud data contract for each equipment class
• AI/ML & Continuous Improvement
• Define and execute product strategy for AI-driven manufacturing intelligence: predictive maintenance, automated quality inspection, process parameter optimization, and autonomous cell control
• Own requirements for the data platform layer that enables ML — feature stores, event streams, model serving infrastructure
• Drive the feedback loop between quality outcomes (first-pass yield, scrap rates) and upstream process adjustments — ensuring the system learns and improves continuously
• Define the operator interaction model for AI recommendations — when to alert, when to auto-act, when to require human confirmation
• Shop Floor Quality & Compliance
• Own the product experience for in-line quality: inspection workflows, non-conformance reporting, root cause analysis tools, and SPC dashboards
• Define how quality data flows back to both the scheduling system (for replan triggers) and the enterprise system (for financial variance reporting)

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