Sr Applied Scientist, Amazon Recommerce India
ADCI - Karnataka•2w ago
IN, KA, BengaluruOnsiteFull-timeSenior Level5+ yrs exp
Top focus
Applied Scientist
- Every product a customer returns is a moment where Amazon either recovers value or writes it off — and India's 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
- pricing recovered inventory dynamically. India's returns network is large, fast-growing
- 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
- adapting proven Worldwide models to India's data, catalog
- operational reality — recalibrating them where distribution, language
- process differ. You will own problems end-to-end, from framing and data through modeling, evaluation
- production deployment, partnering closely with engineering, product
- 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
- recovery value. Develop computer-vision models for defect detection, condition assessment
- anomaly/fraud identification (including inside sealed packaging)
- 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
- re-evaluating for India's return distribution, catalog, languages
- operational constraints
- 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
- retraining — with rigorous attention to calibration, drift
- business-metric impact. Partner cross-functionally with engineering (to productionize), product (to frame problems and measure impact)
- operations (to ground models in how the network actually runs)
- 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 week's returns
- confirming the recovery-value lift is holding. Mid-morning, you dig into a computer-vision problem: improving detection of a damage type that's driving write-offs, using images captured across the returns journey. In the afternoon you work with a Worldwide science team to bring one of their models to India — scoping what retraining and recalibration India's 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
- 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 don't. 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
- Master's 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. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Required skills
PythonJavaRSparkNumPyTensorFlowLLMscikit-learnDistributed Systems