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