Sr Data Scientist, Seller Economics, WW Global Selling - PMO

Amazon is hiring a Sr Data Scientist, Seller Economics, WW Global Selling - PMO in Shanghai, China. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include SQL, data science and machine learning.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Drive seller economics and experience improvements using advanced machine learning, causal inference, and data science models.

SQL

data-sciencemachine-learningcausal-inferenceeconometricspredictive-modelingclustering

Amazon WW Global Selling is looking for a dynamic, highly motivated Sr Data Scientist to join the central PMO team. This is an unique opportunity to:

Play a highly visible role in an exciting and fast paced business
Drive high impact products and initiatives to continuously improve Sellers' experience, growth and profitability
Innovate with advanced seller insights and influence cross-functional and global partners for great ideas and products.
Influence and drive decisions of senior leadership

This role is well-suited for someone with a strong economics or causal ML foundation who wants to apply rigorous statistical thinking to real product decisions at scale. You'll need to be comfortable writing SQL, working with imperfect data, and partnering with stakeholders to turn analysis into product impact. The ideal candidate will be strong at performing deep dives to derive insights on Seller behaviors, identify key pain points and needs from Seller perspective, and keen on AI-powered innovations, including:

1. Science-backed segmentation with a self-iterating feedback loop: Clustering algorithms (k-means, hierarchical, DBSCAN), Feature engineering from catalog and sales data, Feedback-loop model design, Ability to explain model logic to non-technical stakeholders
2. Econometric model to identify high expansion intent sellers: Propensity scoring and intent modeling (logistic regression, gradient boosting, neural), Behavioral sequence modeling, Feature engineering from sparse event-driven data, Classification threshold calibration
3. Post-Enrollment Outcome Models: Causal inference and treatment effect estimation (DiD, PSM, synthetic control, IV), A/B test design, Reinforcement learning
Science-backed graduation signals framework: Time series modeling and threshold detection, Survival analysis or time-to-event modeling, Bayesian updating for threshold calibration, Real-time or near-real-time scoring pipeline design
4. Familiarity with GenAI and LLM frameworks; experience embedding science models into AI-powered products or agents

Key job responsibilities
Key job responsibilities
- Use advanced statistical and machine learning techniques to extract insights from complex, large-scale data sets
- Partner with business/product stakeholders and senior science peers to identify strategic data-driven opportunities to improve the seller experience with a focus on seller economics domain
- Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders
- Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization
- Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment
- Support scalable, self-service data analyses by building datasets for analytics, reporting and ML use cases

About the team
We are WW Global Selling PMO team. We collaborate with WWGS business teams to identify key Seller needs and partner with global product teams to develop tools and solutions to improve Seller experience and sustainable growth.

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
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression

Preferred qualifications

- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team

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

Sr Data Scientist, Seller Economics, WW Global Selling - PMO 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

Sr Data Scientist, Seller Economics, WW Global Selling - PMO 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 August 14, 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

Data ScienceMachine learningCausal InferenceEconometricsPredictive ModelingClusteringSQL

Also common in data scientist jobs at this level

Spark PyTorch Deep learning NLP RAG TensorFlow

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

Tell me about a project where data science was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where causal inference was part of your work. What did you do?
Tell me about a project where econometrics was part of your work. What did you do?
Tell me about a project where predictive modeling was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Data Science, Machine learning, Causal Inference, Econometrics, and Predictive 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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