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Description

Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience?

Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges?

Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team?

If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day.

In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems.


Key job responsibilities
Own end-to-end development of machine learning models for large-scale risk management systems

Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends

Design, develop, validate, and deploy innovative models to production environments

Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency

Collaborate closely with software engineering teams to implement scalable, real-time model solutions

Partner with operations and business stakeholders to translate risk insights into measurable impact

Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring

Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders
Research and implement novel machine learning and statistical methodologies