About the job
Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS Payments and Fraud Prevention team is looking for an innovative Applied Scientist to help keep AWS a safe and trusted environment for millions of customers worldwide. In this role, you will design, build, and deploy machine learning models that detect, prevent, and mitigate fraudulent activity across the AWS ecosystem. You will work with massive, real-world datasets, develop new detection strategies, and apply advanced and practical technologies to tackle ever-evolving threats. You will also explore Generative AI (GenAI) techniques to uncover new fraud patterns and strengthen our fraud defenses. At AWS, we support hundreds of thousands of businesses, powering billions of transactions every day. Fraudsters are constantly innovating — and so are we. If you enjoy thinking like a fraudster, building resilient defenses, and making a real-world impact, we invite you to join us and help shape the future of secure cloud computing.
Responsibilities
Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform.\\\nAnalyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats.\\\nExplore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities.\\\nCollaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions.\\\nExperiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics.\\\nContinuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics.\\\nCommunicate findings and technical insights clearly and effectively to both technical and non-technical audiences.\\\nContribute to the broader fraud prevention strategy, driving innovation and best practices across the organization.
Qualifications
Minimum
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience\\\n3+ years of building models for business application experience\\\nExperience programming in Java, C++, Python or related language\\\nExperience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning\\\nExperience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred
Experience in fraud detection, cybersecurity, anomaly detection, risk modeling, or adversarial machine learning.\\\nHands-on experience applying GenAI techniques such as synthetic data generation, adversarial simulation, or large language model (LLM) insights.\\\nAbility to collaborate across multidisciplinary teams and clearly communicate technical concepts to non-technical audiences.