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 responsibilitiesOwn end-to-end development of machine learning models for large-scale risk management systemsAnalyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trendsDesign, develop, validate, and deploy innovative models to production environmentsApply GenAI/LLM technologies to automate risk evaluation and improve operational efficiencyCollaborate closely with software engineering teams to implement scalable, real-time model solutionsPartner with operations and business stakeholders to translate risk insights into measurable impactEstablish scalable and automated processes for data analysis, model experimentation, validation, and monitoringTrack model performance and business metrics; communicate insights clearly to technical and non-technical stakeholdersResearch and implement novel machine learning and statistical methodologiesBasic Qualifications3+ years of building models for business application experiencePhD, or Master's degree and 4+ years of CS, CE, ML or related field experienceExperience in patents or publications at top-tier peer-reviewed conferences or journalsExperience programming in Java, C++, Python or related languageExperience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingPreferred QualificationsExperience in professional software developmentOur inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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