Job Description

DESCRIPTION

Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS 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.

Key job responsibilities
* Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform.
* Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats.
* Explore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities.
* Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions.
* Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics.
* Continuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics.
* Communicate findings and technical insights clearly and effectively to both technical and non-technical audiences.
* Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization.

BASIC QUALIFICATIONS

- Experience programming in Java, C++, Python or related language
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience applying theoretical models in an applied environment

Job Tags

Full time, Worldwide,

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