Amazon.com’s Buyer Risk Prevention (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com.
The BRP organization is looking for an Applied Scientist for its Payment Risk Mining ML team, whose mission is to combine advanced machine learning techniques to detect negative customer experiences, improve system effectiveness, and prevent bad debt across Amazon.
As an Applied Scientist in Risk Mining, you will be responsible for modeling complex problems, discovering insights, and building risk algorithms that identify opportunities through state-of-the-art techniques including statistical models, deep learning, large language models (LLMs), and agentic AI systems. You will explore and implement state of the art approaches such as graph neural networks, anomaly detection, GenAI-powered investigation agents, and multi-agent systems to automate fraud detection and prevention at scale. You will build and deploy production-ready models and automated systems to improve operational efficiency and reduce bad debt.
You will collaborate effectively with business and product leaders within BRP and cross-functional teams to build scalable solutions. The ideal candidate should be able to apply a breadth of tools, and advanced ML techniques, from traditional machine learning to emerging agentic frameworks, to take ideas from experimentation to production, driving tangible improvements in fraud detection and prevention.
The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high-impact role with goals that directly impact the bottom line of the business.
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
Basic Qualifications
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred Qualifications
- Experience in professional software development
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