Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business

Amazon is hiring a Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business in Shanghai, China. Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Python, SQL and R.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Design and deliver seller segmentation, propensity models, and GenAI-powered features as a senior data scientist for Amazon Business.

PythonSQLRSASMATLABLLMs

ragnlpmachine-learningdata-sciencestatistical-modelinga-b-testing

Team & Project Overview

The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces.

Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement.

Scope of Impact

Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE)

Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions

Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC)

Influence $100M+ annual seller GMS through improved segmentation and contact optimization

Key job responsibilities

Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage.

Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling).

Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions.

Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals.

Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis.

Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership.

Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools.

Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM).

Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.

Basic qualifications

- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Proven track record of end-to-end ML model delivery: problem formulation → feature engineering → training → deployment → monitoring
- Experience designing and analyzing A/B experiments at scale with rigorous statistical methodology
- Demonstrated ability to translate ambiguous business problems into well-scoped science deliverables
- Strong written and verbal communication — ability to present complex findings to non-technical stakeholders
- Experience working with or evaluating AI systems

Preferred qualifications

- Ph.D. in a quantitative field (Statistics, Machine Learning, Economics, Operations Research)
- Experience with NLP/LLM applications (text classification, intent detection, embedding-based retrieval, RAG pipelines)
- Experience in seller/customer segmentation, propensity modeling, or CRM/lifecycle analytics
- Proficiency with distributed computing frameworks (Spark, EMR, Redshift, Hive)
- Experience working in a marketplace or platform business (e-commerce, SaaS, fintech)
- Familiarity with causal inference methods (DID, RDD, synthetic control, instrumental variables)
- Experience mentoring junior data scientists or leading a small science team
- Track record of publishing research papers or creating reusable analytical frameworks
- Knowledge of knowledge graph construction, entity resolution, or identity systems

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.

How we rate this

Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business at Amazon rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in CN, 31, Shanghai.
The job was posted on September 3, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGNLPMachine learningData ScienceStatistical ModelingA B TestingPythonSQL

Also common in data scientist jobs at this level

AWS PyTorch Deep learning TensorFlow MLOps scikit-learn

Pay

Typical data scientist job: $168k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
What NLP problem have you worked on, and how did you measure whether it actually worked?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where data science was part of your work. What did you do?
Tell me about a project where statistical modeling was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, NLP, Machine learning, Data Science, and Statistical Modeling. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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