You will build and lead the measurement, experimentation, and value attribution function for Amazon's Devices & Services organization. Your team is the shared science infrastructure informing production optimization systems that serve 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 simply a traditional analytics or measurement role. You will own an active experimentation mandate by designing and running tests that produce the causal inputs for pricing decisions, marketing optimization, merchandizing options, and portfolio strategy. Your outputs provide the causal evidence base that 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.
Your experimentation organization will directly shape how customers experience Amazon Devices & Services- from the prices they see to the promotions they receive to the features we invest in- by providing the causal evidence that determines where D&S allocates resources to improve the customer journey.
Key job responsibilities
Economic Value (EV)
• Downstream value attribution for all Devices product lines — Impact on Prime, subscription lift, consumer spending, advertising value
• In-period and lifetime incremental value models using causal inference
• Pro Forma P&L methodology alignment with
• Define and own EV coverage metrics, ensuring causally validated measurement expands across the portfolio
• Navigate cross-functional complexity in this high-visibility, multi-stakeholder space
Marketing Science & Measurement
• Build the marketing science function from scratch - incremental impact and ROI across all channels and guiding investment allocation decisions
• Incrementality measurement for marketing spend across all channels — own causal measurement of spend effectiveness and channel coverage
• Establish metrics: marketing spend coverage (causally measured), channel coverage, calibration error % (expected lift vs. experimented lift)
Pricing & Elasticity
• Experiment consistently to push causally validated elasticity outcomes into production systems
• Own elasticity coverage and accuracy metrics
Experimentation Platform Strategy
• Designing and running tests that produce causal inputs for pricing, marketing optimization, merchandising, and portfolio strategy
• Drive experiment cycle time reduction
• Partner with engineering while owning science direction, experiment design, and analytical outputs
• Operate with AI-native workflows — using automated experimentation pipelines and machine-driven evidence synthesis to increase organizational leverage
• Define when measurement confidence is sufficient to trigger autonomous action
A day in the life
Key Metrics You'll Influence
• Marketing spend coverage (causally measured %)
• Marketing channel coverage
• Calibration error % (expected lift vs. experimented lift)
• Elasticity coverage & accuracy
• Experiment cycle time
• EV coverage across portfolio
