Principal Product Manager - Tech, Devices Decision Science
Amazon · Sunnyvale, CA · Project/Program/Product Management--Technical
About this role
Amazon is hiring a principal-level Product Manager based in Sunnyvale, CA. The posting calls out experience with Python, R, Data Structures, Machine Learning. Compensation is listed at $206,900–$279,900 per year.
- Role
- Product Manager
- Function
- product
- Level
- principal
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Sunnyvale, CA
- Department
- Project/Program/Product Management--Technical
- Posted
- May 1, 2026
More roles at Amazon
Job description
from Amazon careersWe are seeking an experienced a Principal Technical Product Manager to own the product strategy and roadmap for quantitative analysis products within Decision Science. This leader will serve as the critical bridge between science teams and business stakeholders, translating complex model outputs into actionable business strategies for key device portfolios. The ideal candidate is equally comfortable interrogating the internals of a machine learning model as they are presenting portfolio strategy recommendations to senior Device leadership. This role requires a rare combination of scientific fluency, product management excellence, and business acumen. You will shape how Amazon Devices leverages quantitative science to make better, faster, and more impactful decisions — from pre-launch forecasting to portfolio optimization. Key job responsibilities In this role, you will: - Define and own the long-term product vision, strategy, and roadmap for quantitative analysis products that support demand forecasting, portfolio construction, and device economics; - Shape strategy for device portfolios by translating science-driven insights into actionable recommendations for product leadership; - Identify high-impact opportunities where quantitative methods can displace or augment judgment-based decision-making - Partner deeply with science teams to understand, evaluate, and challenge model methodologies, assumptions, and outputs — including econometric models, machine learning forecasts, conjoint analyses,…