Sr. Data Scientist , Companion Product & Servi (ComPAS)

About the role

ComPAS Business Insights is the AI-first data science and analytics team powering Amazon's Companion Products & Services portfolio - Accessories, Pre-Owned Business (POB), and Trade-In (TI). We own the full stack: from production-grade data infrastructure and automated reporting to advanced decision science spanning pricing, consumer behavior, marketing targeting, segmentation, and propensity modeling. We are leveraging AI to build intelligent tools that automate workflows, democratize insights, and put self-service analytics at stakeholders' fingertips. Our mission: turn every pricing, marketing, and customer decision into a science-powered, AI-accelerated outcome.

Requirements

• 5+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
• 5+ years of data scientist experience
• 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
• 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
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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Additional Information

ComPAS Business Insights sits at the intersection of pricing, marketing, and consumer science for Amazon's Companion Products & Services portfolio; spanning Accessories, Pre-Owned Business (POB), and Trade-In (TI). AI is fundamentally changing how we solve these problems. As a Sr. Data Scientist, you will drive that transformation: building advanced ML models and AI-powered tools that automate decision science at scale, turning complex pricing, targeting, and segmentation challenges into intelligent, self-improving systems. You will partner with product, marketing, finance, and engineering leaders to translate ambiguous problems into production-ready ML systems and AI-powered tools. Your work will span pricing science, consumer behavior analysis, marketing targeting, propensity score development, and customer segmentation - always with an eye toward how generative AI and foundation models can accelerate, scale, or reimagine the solution. Key job responsibilities Key Job Responsibilities - Own the full lifecycle of model development - from problem framing and exploratory analysis through feature engineering, model design, deployment, and continuous improvement. - Oversees the development of pricing science models, including price elasticity estimation, promotional effectiveness measurement, and optimal pricing recommendations across Accessories, POB, and TI product lines. - Build and refine propensity models and customer segmentation frameworks that enable precision marketing targeting and personalized customer engagement. - Conduct consumer behavior analysis to uncover purchase patterns, cross-sell opportunities, and drivers of performance across the ComPAS portfolio. - Leverage generative AI and LLMs (e.g., Amazon Bedrock, foundation models) to build intelligent tools that automate insights generation, scale analytical workflows, and solve problems that were previously intractable. - Identify and execute opportunities to optimize and automate existing analytical and scientific processes -ntransforming manual, repetitive work into scalable AI-powered pipelines. - Design and run rigorous experiments (A/B testing, causal inference, synthetic control) to measure impact and guide strategic decisions on pricing, marketing, and product. - Build data-driven business cases to prioritize science and AI initiatives, demonstrating measurable impact on revenue and customer outcomes. - Contribute to the broader science community by mentoring data scientists and publishing technical work in internal and external forums. A day in the life Your mornings start with decision science - framing a pricing or targeting problem, writing Python/SQL to prototype a model, or stress-testing a segmentation approach. Afternoons shift to AI tool-building: experimenting with foundation models, designing automation pipelines, or collaborating with engineers on deployment architecture. Between deep work blocks, you're leading problem-framing sessions with PMs, and business leaders, demoing AI prototypes to stakeholders, or hosting a Lunch & Learn that sparks the next automation idea across the team.

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