Product Manager, Decision Science - Devices
Posted Updated
The Decision Science team sits at the intersection of science modeling, business judgment, and cross-functional alignment, translating roadmap plans, competitive signals, and category trends into defensible demand forecasts that power Amazon's device portfolio decisions. We are looking for a Product Manager who is passionate about applying AI and automation to transform how forecasting decisions are made at scale. This is not a traditional PM role: it is a product management position designed for someone who thinks in systems, builds mechanisms, and sees AI as the lever that turns manual judgment into scalable, repeatable intelligence.
You will own the product vision and execution for AI-powered automation across the Decision Science workflow, from pre-launch forecasting and predecessor curation to scenario generation and forecast quality monitoring. You will work shoulder-to-shoulder with scientists, engineers, and Decision Scientists to identify where human judgment is bottlenecked, where models can be augmented, and where automation can eliminate toil while preserving decision quality.
This role offers a defined technical growth path: you will deepen your expertise in machine learning systems, econometric modeling, and AI-driven decision frameworks, with the expectation of evolving into a senior technical product leader who shapes the science-product interface across DSO.
Key job responsibilities
1. AI-Driven Automation
Own the product roadmap for automating key steps in the Decision Science workflow.
Identify repetitive, judgment-light tasks across database validation, cross-functional review, impact assessment, and documentation, and design AI-assisted solutions that reduce cycle time while maintaining forecast integrity.
2. Intelligent Scenario Generation & Portfolio Optimization
Develop automated anomaly detection that flags when science outputs (e.g., substitution patterns, CCARD mix-down) diverge from business reality, reducing reliance on manual review.
3. Streamline Survey Design
Build mechanisms that auto-generate survey designs from the latest product roadmap (including in-concept programs), translate results into calibrated willingness-to-pay inputs, and feed them systematically into LTD models. Create dashboards and alerting systems that track survey cadence compliance and flag gaps in feature coverage before they become planning blind spots.
4. Cross-Functional Partnership & Stakeholder Alignment
Collaborate daily with Scientists, Engineering, and Business Teams to ensure AI/automation investments are grounded in real workflow pain points.
Coordinate with Product GMs and PL Finance teams to align automated forecast outputs with planning cycle requirements (OPX, 3/5 Year Planning).
Translate complex technical capabilities into clear business value narratives for leadership reviews.
You will own the product vision and execution for AI-powered automation across the Decision Science workflow, from pre-launch forecasting and predecessor curation to scenario generation and forecast quality monitoring. You will work shoulder-to-shoulder with scientists, engineers, and Decision Scientists to identify where human judgment is bottlenecked, where models can be augmented, and where automation can eliminate toil while preserving decision quality.
This role offers a defined technical growth path: you will deepen your expertise in machine learning systems, econometric modeling, and AI-driven decision frameworks, with the expectation of evolving into a senior technical product leader who shapes the science-product interface across DSO.
Key job responsibilities
1. AI-Driven Automation
Own the product roadmap for automating key steps in the Decision Science workflow.
Identify repetitive, judgment-light tasks across database validation, cross-functional review, impact assessment, and documentation, and design AI-assisted solutions that reduce cycle time while maintaining forecast integrity.
2. Intelligent Scenario Generation & Portfolio Optimization
Develop automated anomaly detection that flags when science outputs (e.g., substitution patterns, CCARD mix-down) diverge from business reality, reducing reliance on manual review.
3. Streamline Survey Design
Build mechanisms that auto-generate survey designs from the latest product roadmap (including in-concept programs), translate results into calibrated willingness-to-pay inputs, and feed them systematically into LTD models. Create dashboards and alerting systems that track survey cadence compliance and flag gaps in feature coverage before they become planning blind spots.
4. Cross-Functional Partnership & Stakeholder Alignment
Collaborate daily with Scientists, Engineering, and Business Teams to ensure AI/automation investments are grounded in real workflow pain points.
Coordinate with Product GMs and PL Finance teams to align automated forecast outputs with planning cycle requirements (OPX, 3/5 Year Planning).
Translate complex technical capabilities into clear business value narratives for leadership reviews.