Applied Scientist: Real-Time Payments & Fraud Detection

Amazon Web Services (AWS); New York (NY); On-site; USD 172,000 - 223,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Development Center U.S., Inc. in New York, NY is seeking an Applied Scientist to advance fraud prevention on one of the world’s largest cloud platforms.

You will design, build, and deploy end-to-end machine learning models to detect and prevent fraudulent activity across AWS payments and usage ecosystems. You will work with massive datasets, apply traditional ML and GenAI techniques to uncover threats, and own the full lifecycle from data extraction to production deployment.

Qualifications

• 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 programming in Java, C , Python or related language.
• 3 years of practical work applying ML to solve complex problems for large-scale applications.
• 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.

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 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 and evaluation metrics with rapid time-to-production.
• Collaborate with engineering, product, and operations teams to translate business needs into scalable technical solutions.
• Communicate findings and technical insights clearly to stakeholders at all levels.
• Contribute to the broader fraud prevention strategy and best practices across the organization.

Skills

Statistical modeling

Machine learning

Python

Java/C++

Experiment design

Education

PhD or Master’s in ML

Tools

SAS

Matlab

SQL

Job description

Amazon Development Center U.S., Inc. in New York, NY is seeking an Applied Scientist to advance fraud prevention on one of the world’s largest cloud platforms.

You will design, build, and deploy end-to-end machine learning models to detect and prevent fraudulent activity across AWS payments and usage ecosystems. You will work with massive datasets, apply traditional ML and GenAI techniques to uncover threats, and own the full lifecycle from data extraction to production deployment.