Amazon

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

PREFERRED QUALIFICATIONS

• Experience in fraud detection, cybersecurity, anomaly detection, risk modeling, or adversarial machine learning.
• Hands-on experience applying GenAI techniques such as synthetic data generation, adversarial simulation, or large language model (LLM) insights.
• Experience designing and deploying machine learning models in production environments.
• Ability to collaborate across multidisciplinary teams and clearly communicate technical concepts to non-technical audiences.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our 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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.