Description

Amazon's Devices & Services (D&S) organization builds and markets consumer products used by hundreds of millions of customers worldwide—including Ring, Blink, Alexa, Kindle, Fire TV, and more. Within D&S, the Demand Science & Optimization (DSO) organization is responsible for the science, data, and products that drive pricing, marketing measurement, demand forecasting, and spend optimization across 50+ product lines in 10+ countries.

Today, the vast majority of D&S's marketing and pricing allocation decisions rely on proxy assumptions rather than direct experimental evidence. We are building the Experimentation & Economic Value Platform to change that: a centralized system that connects experimentation across pricing, marketing, and merchandising into causal science and forecasting models, so every investment decision is backed by evidence rather than assumption.

We are seeking a Principal Product Manager-Tech to own this product end-to-end. You will define the conceptual architecture for a suite of interconnected services—experimentation engines spanning pricing, marketing, and economic value measurement, along with a parameter store, experiment registry, and coordination layer—and partner closely with engineering to build them. A critical part of the role is connecting science outcomes to production systems: ensuring that experiment results, model parameters, and causal estimates flow reliably into the downstream decision products that consume them. You will own the product and experimentation roadmap, define success metrics on usage, engagement, and efficiency, build a streamlined intake process, and drive integration with downstream science and business-facing products. This is a high-visibility, high-autonomy role where you will shape how Amazon's largest consumer hardware business makes data-driven decisions.


Key job responsibilities
Define the Conceptual Architecture
• Define and deliver the conceptual architecture for the platform's experimentation engines (pricing, marketing, and economic value measurement), parameter store, experiment registry, coordination layer, and results store
• Define integration points with downstream science models (e.g., incrementality modeling, demand forecasting) and business-facing products (e.g., spend optimization, scenario planning, and emerging optimization products)
• Partner closely with engineering leadership to translate the conceptual architecture into technical designs, and sequence the development roadmap

Own the Product Vision, Roadmap & Prioritization
• Define and drive the product vision, tenets, and multi-year roadmap for the Experimentation & Economic Value Platform
• Own the experimentation intake process, roadmap, and prioritization across pricing, marketing, and economic value workstreams; prioritize science and engineering bandwidth in partnership with DSO Product and Engineering managers
• Define, prioritize, and track product success metrics: platform adoption, usage, engagement, experiment velocity, model calibration improvement, and iROAS gains

Build a Scalable, AI-Augmented Experimentation Product
• Define how AI agents and automated workflows accelerate experimentation design, execution, monitoring, and insights across the full experimentation lifecycle
• Own and deliver experimentation platforms for joint marketing and pricing experiments
• Implement monitoring systems in partnership with data science and software development—covering data quality, statistical significance, and optimization recommendations—across marketing experimentation and economic value refresh processes

Drive Rigorous Experiment Design & Execution
• Translate user stories and business questions into statistically rigorous randomized controlled trials (RCTs) at the geo and user level
• Own experiment prioritization, design quality, and execution standards globally—ensuring consistency across macro experiments (geo-level strategic allocation questions) and micro experiments (platform-native A/B tests optimizing creative, audience, and bid strategies)
• Partner with economists and data scientists to define RCT validity criteria, create reusable design templates, develop power calculation guidelines, and establish quality bars to ensure direct usability by the business

Lead Stakeholder Engagement & Operational Excellence
• Establish regular business reviews with senior leadership (VP+) to communicate platform progress, experiment impact, and investment priorities
• Build a streamlined, trackable intake process that replaces ad-hoc coordination with structured experiment requests, transparent prioritization, and automated scheduling