Applied Scientist, Payments & Fraud Prevention, AWS Payments & Fraud Prevention
Core
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.
Role type
Applied Scientist (Machine Learning)
Builds
Production ML models for real-time fraud detection and prevention
Domain
Cloud computing, Payments, Fraud Prevention
Deliverable
production ML models
Required skills
Statistical modeling, traditional machine learning, Generative AI (GenAI), large language models (LLMs), synthetic data generation, feature engineering, model lifecycle management, adversarial behavior analysis, advanced SQL, automation scripting
Preferred skills
Neural deep learning methods, AWS Infrastructure knowledge, AWS services knowledge (compute, storage, network), Unix/Linux experience, professional software development
Technologies
Python, Java, C++, R, Weka, SAS, Matlab, SQL, LLMs
Responsibilities
Design and deploy ML models for fraud detection; source and analyze large-scale behavioral and transactional datasets; apply GenAI techniques to enhance fraud signal discovery; own the full model lifecycle from data extraction to productionalization; monitor model performance and improve robustness against evolving fraud tactics; experiment with new detection strategies and algorithms; collaborate with engineering and product teams to translate business needs into technical solutions.
Seniority
Senior, hands-on IC
