Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon maintain the integrity of its product catalog and elevate Amazon Selling Partners to be the most successful by preventing abuse? Are you excited by the prospect of creating state-of-the-art algorithms to solve real world problems in catalog management? Do you like to own end-to-end business problems/metrics and directly impact the quality and trustworthiness of Amazon's product listings? Do you enjoy collaborating in a diverse team environment?
If yes, then you may be a great fit to join the Amazon Catalog Abuse Detection and Prevention Team. We are looking for a talented scientist who is passionate about building advanced machine learning systems that help detect and prevent catalog abuse across millions of product listings every day and scale up our operation with automation.

Key job responsibilities
- Use machine learning and statistical techniques to create scalable catalog abuse detection solutions that identify listing violations, selling partner integrity, and content manipulation
- Innovate with the latest GenAI technology to build highly automated solutions for efficient catalog monitoring, content verification, and automated listing compliance
- Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment
- Learn, explore and experiment with the latest machine learning advancements to protect selling partner trust and integrity


A day in the life
You'll be working closely with business partners and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation.

You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.