Sr Applied Scientist, Amazon Recommerce India

Every product a customer returns is a moment where Amazon either recovers value or writes it off and Indias ReCommerce business is on a multi-million-dollar mission to recover more of it more intelligently at scale. Machine learning is the core lever: predicting whether a returned unit is sellable without a human touching it detecting damage and fraud inside sealed packaging from images routing each unit to its highest-value disposition and pricing recovered inventory dynamically. Indias returns network is large fast-growing and structurally different from other geographies a rich high-impact environment for an Applied Scientist to build models that move real financial and customer-experience metrics.

We are hiring an Applied Scientist to build and adapt the ML that powers India ReCommerce. You will work at the intersection of two mandates: building India-first models for problems unique to our market and adapting proven Worldwide models to Indias data catalog and operational reality recalibrating them where distribution language and process differ. You will own problems end-to-end from framing and data through modeling evaluation and production deployment partnering closely with engineering product and operations.

Key job responsibilities
Build ML models for automated returns grading predicting the salability of returned units from structured and unstructured signals so units can be evaluated with zero or minimal human touch improving speed accuracy and recovery value.

Develop computer-vision models for defect detection condition assessment and anomaly/fraud identification (including inside sealed packaging) and for establishing chain-of-custody and damage attribution across the returns journey.

Build disposition-prediction and routing models that direct each unit to its highest-value recovery path (resale repair liquidation donation recycle) as early as possible in the network.

Develop pricing and recovery-optimization models for liquidation and resale moving from flat rates toward dynamic grade- and condition-aware pricing.

Adapt Worldwide ML models to India retraining recalibrating and re-evaluating for Indias return distribution catalog languages and operational constraints and closing the gaps that prevent a direct lift-and-shift.

Own the full model lifecycle problem framing data pipelines feature engineering training offline/online evaluation monitoring and retraining with rigorous attention to calibration drift and business-metric impact.

Partner cross-functionally with engineering (to productionize) product (to frame problems and measure impact) and operations (to ground models in how the network actually runs) and use modern GenAI/LLM tooling to accelerate research and delivery.

A day in the life
You start by reviewing the performance of a grading model in production checking calibration and drift against last weeks returns and confirming the recovery-value lift is holding. Mid-morning you dig into a computer-vision problem: improving detection of a damage type thats driving write-offs using images captured across the returns the afternoon you work with a Worldwide science team to bring one of their models to India scoping what retraining and recalibration Indias data requires then pair with an engineer to move your latest model toward production behind a clean evaluation gate. You close by framing a new problem with a product partner: quantifying the opportunity defining the label and success metric and sketching the modeling approach.

About the team
India ReCommerce owns the systems and science that turn returned and unsellable inventory into recovered value and a better customer experience. You will join a team building an increasingly automated ML-driven returns network leveraging Worldwide platforms where they fit and building India-first capabilities where they dont. It is a high-ownership environment with a direct line from your models to measurable business and customer outcomes.

- 6 years of building machine learning models for business application experience
- PhD or Masters degree
- Experience programming in Java C Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.
- Experience with large scale distributed systems such as Hadoop Spark etc.

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