Principal Product Manager - Tech, Devices Decision Science

About the role

The Decision Science team replaces judgment-based decisions with science-driven forecasts and quantitative analysis. We partner with engineers, scientists, and product managers to apply advanced science to forecast demand and efficiently allocate Amazon Device products across the portfolio. We build and operate econometric and machine learning models that power lifetime demand forecasting, rapid reforecasting, mix adjustments, and portfolio optimization for product launches spanning eReaders, Tablets, Fire TV, Ring, Blink, and Alexa+-enabled devices.

Requirements

• 10+ years of product or program management, product marketing, business development or technology experience
• Experience with feature delivery and tradeoffs of a product
• Experience owning/driving roadmap strategy and definition
• Experience with end to end product delivery
• Experience contributing to engineering discussions around technology decisions and strategy related to a product
• Bachelor's degree in a quantitative field, or Bachelor's degree and 2+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
• Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
• 5+ years of experience working directly with data science, machine learning, or quantitative analysis teams
• Demonstrated ability to understand and critically evaluate statistical models, machine learning algorithms, and their business applications
• Experience defining product strategy and roadmaps for data-intensive or analytics products
• Track record of influencing senior leadership with data-driven recommendations
• Experience working directly with Engineers on product enhancements
• Experience in project management methodologies, business analysis, or process improvement
• Knowledge of methods for statistical inference (e.g. regression, experimental design, significance testing)
• Experience directly managing scientists or machine learning engineers
• Knowledge of data analysis languages (e.g., R, Stata, Python, etc.) or other statistical software
• Master's degree or PhD in a quantitative discipline (e.g., Economics, Statistics, Operations Research, Data Science)
• Experience with demand forecasting, portfolio optimization, or econometric modeling in a product or business context
• Experience in consumer electronics, hardware, or devices businesses
• MBA or equivalent business strategy experience is a plus
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• Los Angeles County applicants: Job duties for this position incl

Additional Information

We 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, and causal inference techniques - Dive deep into science model outputs to validate accuracy, identify edge cases, and ensure business applicability; and, - Translate complex quantitative concepts into clear, compelling narratives for non-technical stakeholders. - Conduct leadership reviews to present science-backed portfolio recommendations - Build and maintain strong relationships with cross-functional partners including supply chain, finance, marketing, and hardware engineering teams. - Establish mechanisms to measure the business impact of science-driven decisions and continuously improve model adoption and trust.

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