Senior Economist

• IES Payments is building a first-of-its-kind Economic Profit (EP) framework to measure the
• full economic value of Amazon Payment Products (APPs) and payment-led promotional
• levers across 9 emerging markets. Today, program leaders cannot credibly quantify the total value their products create beyond direct revenue contribution — EP changes that.

• We are looking for an Economist who will own the incrementality science that underpins
• this framework. You will design, evolve, and defend the econometric methodologies that
• measure how payment instruments create value — within the transaction (Day-0) and over
• the 365-day customer lifetime (DSI). You will be the single-threaded science owner for how
• we compute and justify incrementality across always-on instruments, promotional constructs, weblabs, and behavior-change programs.

• This is not a support role. You will directly shape how Amazon measures the ROI of its
• payment investments across IES — and your work will feed into S-Team level planning and
• investment decisions.

• Key job responsibilities
• What You'll Do
• Incrementality Methodology — Design & Evolve
• ● Own end-to-end design of incrementality computation logic across EP use cases: always-on APPs, one-time promotions, weblab measurement, and ongoing behavior-change programs (e.g., UPI adoption rewards)
• ● Evolve the Day-0 incrementality methodology — the novel component that captures value created within the current transaction itself, where standard DSI excludes the event day
• ● Design econometric approaches for use cases where GCCP fails (weblabs with instrument dynamics) and where traditional promo measurement breaks down (investment-phase → payoff-phase programs)
• ● Develop and validate proxy logic for segment-wise EP cuts in the absence of dedicated compute infrastructure

• CBA & Cross-Functional Alignment
• ● Lead the formal methodology defense with CBA V-Team — every surrogate, every computation, every modification to Watson's default workflow requires rigorous scientific justification and CBA sign-off
• ● Align with DEX and Stores Finance on the science behind cost-to-serve incrementality metrics (CoP avoidance, UPB savings, CoBW reduction)
• ● Navigate the surrogate onboarding challenge: new payment surrogates can cannibalize value currently attributed to existing Marketplace HVAs — you will need data-backed arguments to bring Stores leadership on board, not just science correctness
• ● Build and maintain the defensibility of EP methodology against internal scientific scrutiny

• Partnership with Data Science
• ● Work hand-in-hand with the EP Data Scientist to build a holistic case for payment HVA incrementality — you bring the econometric logic, they bring the predictive model justification; both must align for CBA acceptance
• ● Jointly defend surrogate onboarding proposals where the economist provides the causal framework and the data scientist provides the feature-level model evidence
• ● Translate econometric findings into business narratives for VP+ leadership reviews

• Geographic Expansion
• ● Adapt and calibrate EP methodology for Brazil, Mexico, MENA, and APAC markets — each with distinct instrument mixes, data maturity levels, and marketplace economics
• ● Determine where the India-built methodology transfers cleanly and where market-specific econometric adaptations are needed

• About the team
• Our team is dedicated to applying economic thinking to some of Amazon's most impactful business questions. We bring together economists with expertise spanning causal inference, structural modeling, and applied microeconomics to deliver insights that guide long-term strategy. We value intellectual rigor, open debate, and a collaborative spirit that makes everyone stronger.

We are building toward a future where economic analysis is deeply embedded in decision-making at every level. If you want to work alongside thoughtful, curious colleagues and see your work shape how Amazon operates, this is the team for you.

Basic qualifications

• - Experience with statistical analytics and programming languages such as R, Python, Ruby, etc.
• - Experience in A/B testing
• - Experience using data and metrics to influence senior leadership and drive organizational change
• - PHd or Master's degree in Economics, Econometrics, Statistics, or a quantitative field, with 5+ years of industry experience in applied economics or causal inference
• - Deep expertise in causal inference methodologies — difference-in- differences, instrumental variables, regression discontinuity, synthetic control, treatment effect estimation
• - Demonstrated experience designing and defending incrementality or attribution measurement frameworks — either in tech, financial services, or marketplace settings.
• - Ability to communicate complex econometric concepts to non-technical business and finance leaders
• - Comfort with ambiguity — this role requires designing methodology for problems where no clean playbook exists

Preferred qualifications

• - Experience in analytics and applied economics
• - Experience in developing and executing an analytic vision to solve business-relevant problems
• - Experience in building statistical models using R, Python, STATA, or a related software
• - Experience in industry, consulting, government or academic research
• - PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
• - Experience with customer lifetime value (CLV) or downstream value modeling — predicting long-horizon behavioral effects of interventions
• - Familiarity with Amazon's CBA ecosystem (Dr. Watson, DSI/DSE, GCCP, HVA frameworks) or equivalent internal measurement platforms at other tech companies
• - Experience navigating cannibalization and attribution disputes across organizational boundaries — situations where your measurement shifts value between teams
• - Prior work on payment instrument economics, affordability products, or fintech ROI measurement
• - Experience with large-scale causal inference on transactional data (100M+ transactions/month)
• - Publications or internal whitepapers on applied econometric methodology

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