Applied Scientist, Global Risk Intelligence and Prevention, Seller Abuse Prevention

We are seeking an exceptional Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon"s store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon"s catalog worldwide.

You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics.

Key job responsibilities

• Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale
• Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels
• Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience
• Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas
• Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness

A day in the life

Day-to-day you can expect to:

• Explore datasets to understand predictors and patterns of abuse
• Work with product, program, and engineering stakeholders to build solutions into production that will last
• Identify new and emerging abuse vectors as abusers get more sophisticated
• Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions.

About the team

Seller Abuse Prevention detects abuse across 4 distinct spaces of abuse: catalog, review, financial risk, and discovery/competitor abuse. Seller Abuse Prevention is embedded in a team of scientists that tackle cross-spanning risk prevention problems. The team has expertise across graph networks, LLMs/agents, and fraud detection.