Data Scientist, WWGS SSG Strategic Initiative Development

Amazon is hiring a Data Scientist, WWGS SSG Strategic Initiative Development 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.
The posting mentions RAG and NLP among its tools and skills.
Amazon has 21,000+ other open roles on Level.
Posted in the last few days.

AI in this role

ragnlp

As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands.

Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact.

The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases.

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, WWGS SSG Strategic Initiative Development at Amazon rates 92 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, WWGS SSG Strategic Initiative Development 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 October 26, 2026.
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

RAGNLP

Also common in data scientist jobs at this level

AWS PyTorch Deep learning scikit-learn TensorFlow MLOps

Pay

Typical data scientist job: $166k

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?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: RAG and NLP. 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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