Director, Economist - Head of Experimentation & Value, Devices Science Optimization (DSO)

You will build and lead the economics research agenda for measurement, experimentation, and value attribution for Amazon's Devices & Services organization. Your team is the "truth layer" of the Intelligence Core — the shared economics and causal inference capability that serves all Devices product lines, marketing pods, and Finance leadership with causal evidence of what Devices are worth and whether our investments are working.
This is not a traditional analytics or measurement role. You will own an active research program in experimentation design — identifying and executing the causal studies that produce the causal inputs for pricing decisions, marketing optimization, and portfolio strategy. Your outputs provide the causal evidence base that L8 peers and senior leadership consume to make billions of dollars in investment decisions across the D&S portfolio. You will also own the economic models that validate and drive execution across the full surface area of marketing spend for devices and services.

Key job responsibilities
Economic Value:
• Downstream value attribution for all Devices product lines — Impact on Prime, subscription lift, consumer spending, advertising value
• Alexa+ value isolation and cross-PL attribution
• Causal frameworks connecting device sales to Prime acquisition, subscription retention, and ecosystem engagement

Marketing Science & Measurement:
• Build the marketing science function from scratch
• Incrementality measurement for marketing spend across all channels
• Attribution methodology, measurement standards, and cross-pod governance
• Marketing ROI frameworks for use by category marketers
• CCM certification methodology and scenario planning models for optimal investment allocation

Experimentation:
• Owning the estimation methodology, identification strategies, data inputs/outputs, and refresh cadence
• You will build this team's analytics function with AI at its core from day one
• Experimentation governance — managing interference across teams, setting standards for causal validity
• Evaluation framework for AI agents and autonomous optimization systems