Data Scientist, SPX AI Lab, SPX Science

About the role

SPX AI Lab is a growing team of scientists driving the research and development of the next generation of GenAI experiences that empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. We strive to radically simplify the seller experience, lowering the cognitive burden of selling on Amazon by making it easy to accomplish critical tasks such as launching new products, understanding and complying with Amazon's policies and taking actions to grow their business.

Requirements

• 2+ years of data scientist experience
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 1+ years of working with or evaluating AI systems experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
• Knowledge of machine learning concepts and their application to reasoning and problem-solving
• Experience in a ML or data scientist role with a large technology company
• Experience in defining and creating benchmarks for assessing GenAI model performance
• Experience working on multi-team, cross-disciplinary projects
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• Experience effectively communicating complex concepts through written and verbal communication
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, o

Additional Information

The Selling Partner Experience (SPX) organization strives to make Amazon the best place for Selling Partners to do business. The SPX AI Lab team is building the AI capabilities powering the Selling Assistant, Amazon's conversational assistant experience for Selling Partners. The Selling Assistant is a trusted partner and a seasoned advisor that's always available to enable our partners to thrive in Amazon's stores. It takes away the cognitive load of selling on Amazon by providing a single interface to handle a diverse set of selling needs. The assistant always stays by the seller's side, talks to them in their language, enables them to capitalize on opportunities, and helps them accomplish their business goals with ease. It is powered by the latest advances in Generative AI, going beyond a typical chatbot to provide an intuitive, intelligent, agentic and personalized experience to sellers running real businesses, large and small. Do you want to join an innovative group of scientists, engineers, and product managers who use advanced analytical, statistical, and machine learning techniques to help Amazon create a delightful Selling Partner experience? Are you excited about uncovering insights from massive-scale data, measuring the impact of AI-driven features, and shaping product strategy through rigorous analysis? Do you want to be part of one of Amazon's most strategic initiatives to understand and improve seller experience? If yes, the SPX AI Lab may be the perfect fit for you. Key job responsibilities -- Respond to Seller feedback and implement fix in Gen AI solution to enhance Seller experience -- Drive deep-dive analytical studies to understand seller pain points, evaluate feature performance, and identify opportunities to improve the Selling Partner experience. -- Design and execute robust causal inference and measurement frameworks, including A/B testing, quasi-experiments, and observational causal methods (e.g., diff-in-diff, synthetic control, propensity score methods). -- Develop scalable analytical pipelines for impact measurement, KPI development, metric integrity validation, and long-term business monitoring. -- Apply NLP and statistical modeling techniques-including topic modeling, clustering, semantic similarity, and classification-to uncover insights from unstructured seller interactions, feedback, and content. -- Partner with scientists, engineers, economists, and product managers to translate ambiguous problems into structured analytical approaches and influence product roadmaps with data-driven recommendations. -- Build and maintain automated analytics tools and dashboards to democratize insights for product, science, and engineering teams. -- Collaborate scientists to evaluate model-driven features, quantify impact, and ensure mechanisms are grounded in rigorous measurement. -- Research and experiment with new analytical and measurement methodologies, ensuring Amazon leverages the latest best practices in causal inference, NLP, and GenAI.

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