Job description

Are you passionate about building machine learning systems that protect one the world's largest cloud platform? The AWS Payments & Fraud Prevention (P&FP) Science team is looking for a driven Applied Scientist to help safeguard AWS and its millions of customers from evolving fraud threats. In this role, you will design, build, and deploy end-to-end machine learning models that detect and prevent fraudulent activity. You will work with massive, real-world datasets across the AWS payments, signup and usage ecosystem, develop new detection strategies, and take models from concept to production. You will also apply Generative AI (GenAI) techniques to enhance fraud signal discovery and strengthen our detection capabilities. At AWS, we process billions of transactions every day for hundreds of thousands of businesses worldwide. Fraud patterns shift constantly and our defenses must stay ahead. If you enjoy owning problems end-to-end, shipping models that make real-time decisions at scale, and making a direct impact on customer trust and financial protection, we invite you to join us and help shape the future of fraud prevention at AWS. Key job responsibilities Design, build, and deploy end-to-end machine learning models and rules to detect, prevent, and mitigate fraudulent activities across the AWS payment and usage ecosystem. Source, extract, and analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. Apply hands-on expertise in statistical modeling, traditional machine learning, and analytics to identify and isolate issues across the fraud landscape. Explore and apply GenAI techniques, including large language models (LLMs) and synthetic data generation, to enhance fraud detection capabilities. Own the full model lifecycle — from data extraction and feature engineering through evaluation, productionalization, and deployment. Continuously monitor model and rule performance and improve robustness against adversarial behaviors and evolving fraud tactics. Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics with a focus on rapid time-to-production. Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. Communicate findings and technical insights clearly and effectively to both technical and non-technical stakeholders at all levels. Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. A day in the life You will have the opportunity to enhance our existing models and develop new ones that have a direct impact on the business from reducing financial losses to protecting customer accounts in real time. You will own your models end-to-end, from sourcing data and building features through evaluation and productionalization, and you will be expected to move quickly as fraud threats evolve. As part of this role, you will also support core fraud operations reviewing model outputs, tuning detection thresholds, and ensuring our mechanisms are performing as expected in production. This operational closeness to the data and to real fraud cases is what gives our scientists a unique edge: you will develop a deep, practical understanding of how fraud actually works, which directly sharpens the models and strategies you build. It is this combination of science and operational insight that makes our team's work so impactful. Your role will also allow you to leverage your customer-obsession skills by thoughtfully considering the user experience and ensuring it is not adversely affected by the mechanisms you design. You will explore new techniques, including Generative AI, to stay ahead of increasingly sophisticated adversaries. About the team Our team plays a crucial role in safeguarding secure and profitable business operations. Our mission is to position AWS as the most cost-effective and user-friendly cloud service provider by protecting legitimate customer experiences from the financial, operational, and reputational impacts of fraudulent activities. We develop services that prevent, detect, contain, and mitigate the actions of fraudulent and malicious users. As part of an analytics-driven team, you will leverage large-scale data to inform business decisions, respond to fraud, and automate decision-making at scale. Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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. About 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 AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. 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. - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - 3+ years of building models for business application experience - 5+ years of designing experiments and statistical analysis of results experience - Experience programming in Java, C++, Python or related language - 3+ years of practical work applying ML to solve complex problems for large-scale applications experience - Experience with R, Python, Weka, SAS, Matlab or other statistical/machine learning software - Experience in scripting for automation (e.g. Python) and advanced SQL skills. - Experience working in a large team or fast-paced corporate environment - PhD or equivalent Master's degree in Machine Learning, Artificial Intelligence, Mathematics, Statistics, Computer Science, Operations Research or in another highly quantitative field - Ability to develop and deploy (in partnership with engineers) Machine Learning models that power specific applications - Skilled in various Statistical and traditional Machine Learning methods such as tree-based models - Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts - Experience using Unix/Linux - Experience in professional software development - Experience with neural deep learning methods and machine learning - Knowledge of AWS Infrastructure - Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies - 4+ years of applied ML experience in a quantitative filed - Predictive Analytics 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 amazon.jobs for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at amazon.jobs . USA, NY, New York - 172,400.00 - 223,400.00 USD annually

Requirements

Must-haves

Key job responsibilities
Design, build, and deploy end-to-end machine learning models and rules to detect, prevent, and mitigate fraudulent activities across the AWS payment and usage ecosystem.
Source, extract, and analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats.
Apply hands-on expertise in statistical modeling, traditional machine learning, and analytics to identify and isolate issues across the fraud landscape.
Explore and apply GenAI techniques, including large language models (LLMs) and synthetic data generation, to enhance fraud detection capabilities.
Own the full model lifecycle — from data extraction and feature engineering through evaluation, productionalization, and deployment.
Continuously monitor model and rule performance and improve robustness against adversarial behaviors and evolving fraud tactics.
Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics with a focus on rapid time-to-production.
Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions.
Communicate findings and technical insights clearly and effectively to both technical and non-technical stakeholders at all levels.
Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization.
A day in the life
About the team
Diverse Experiences

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningawsaimljavapythonsql

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave