Applied Scientist II, AWS Fraud Prevention
Amazon Web Services•Published 1 days ago•First seen 1 days ago
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
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 and deploy machine learning models to detect and mitigate fraud at scale. You will also have the opportunity to explore Generative AI (GenAI) techniques to uncover new fraud patterns and stay ahead of evolving threats.
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.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
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 and deploy machine learning models to detect and mitigate fraud at scale. You will also have the opportunity to explore Generative AI (GenAI) techniques to uncover new fraud patterns and stay ahead of evolving threats.
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.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
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, machine learning, engineering, or related fields
- 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.