Sr Data Scientist, Amazon Global Selling - PMO

Amazon; Akron (OH); On-site; USD 130,000 - 170,000; Full time

Job summary

Amazon Global Selling is seeking a Sr Data Scientist to join the Selection & Pricing Intelligence team. You will build models, analyze cross-marketplace data, and influence senior leadership with quantitative insights.

You will design end-to-end data science workflows, collaborate with product and domain strategists, and mentor junior analysts. This role offers a challenging, fast-paced environment with global impact.

Qualifications

• 5+ years of data querying languages experience such as SQL and scripting languages.
• 4+ years of data scientist experience.
• Experience with statistical models such as multinomial logistic regression.

Responsibilities

• Use advanced statistical and machine learning techniques on cross-marketplace data.
• Extend and validate demand models to support selection opportunity sizing.
• Build and own pricing measurement frameworks with leadership-level rigor.
• Partner with stakeholders to identify data-driven opportunities across selection and pricing.
• Test framework generalizability across marketplaces and channels.
• Communicate findings to technical and non-technical audiences.

Skills

SQL

Python

R/SAS

Statistical modeling

Tools

AWS QuickSight

Tableau

R Shiny

Job description

Sr Data Scientist, Amazon Global Selling - PMO

Job ID: 10506359 | Amazon (Shanghai) International Trading Company Limited

Amazon WW Global Selling is looking for a dynamic, highly motivated Sr Data Scientist to join the Selection & Pricing Intelligence team. This is a unique opportunity to:

• Play a highly visible role in an exciting and fast-paced business
• Drive high-impact frameworks and initiatives that shape which selection Amazon prioritizes globally and how competitively it’s priced
• Innovate with advanced demand and pricing models, influencing cross-functional and global partners on selection strategy and competitive positioning
• Influence and drive decisions of senior leadership, with your models feeding directly into VP-level reviews

This role is well-suited for someone with a strong statistics, causal ML, or economics foundation who wants to apply rigorous quantitative thinking to real selection and pricing decisions at scale. You'll need to be comfortable writing SQL, working with imperfect and fragmented cross-marketplace data, and partnering with domain strategists to turn analysis into business action. The ideal candidate will be strong at deriving insight from complex demand and pricing signals, testing whether frameworks generalize across marketplaces and channels, and keen on where AI can scale repeatable analytical judgment.

Key job responsibilities

• Use advanced statistical and machine learning techniques to extract insights from complex, large-scale, cross-marketplace data sets spanning selection demand and pricing competitiveness
• Extend and validate demand models (Unmet Demand Model and related frameworks) to support selection opportunity sizing, including entitlement methodology and compliance-adjusted pool sizing
• Build and own pricing measurement frameworks (e.g., price-competitiveness decomposition, competitive positioning metrics) with rigor suitable for senior leadership review
• Partner with business/product stakeholders and senior domain strategists to identify strategic, data-driven opportunities across both the selection and pricing intelligence domains
• Test whether existing frameworks generalize across marketplaces, channels, or seller cohorts — surfacing where a model breaks down before it drives a flawed business decision
• Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders
• 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
• Stay current on data science and AI tooling, and help identify which analytical workflows are strong candidates for automation

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

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Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.