Senior Economist

Amazon · Retail & E-commerce

Bengaluru, Karnataka, India
On-site
Posted yesterday
Consulting & Strategy
full-time

About the job

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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