Applied Scientist, AWS Payments and Fraud Prevention
Core
Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activity across the AWS ecosystem using massive real-world datasets and Generative AI techniques.
Role type
Applied Scientist (Fraud Prevention & ML)
Builds
Scalable fraud detection systems and GenAI-based defense mechanisms for AWS
Domain
Cloud Security / Financial Fraud / Machine Learning
Deliverable
production ML models
Required skills
Deep learning model architecture design, deep learning training and optimization, model pruning, algorithms and data structures, numerical optimization, parallel and distributed computing, high-performance computing, Java, C++, Python
Preferred skills
Fraud detection, cybersecurity, anomaly detection, risk modeling, adversarial machine learning, synthetic data generation, large language model (LLM) insights
Technologies
Generative AI, LLMs, synthetic data generation, adversarial simulations
Responsibilities
Design and deploy ML models for fraud detection, analyze large-scale behavioral and transactional datasets, experiment with GenAI techniques, prototype new detection strategies, monitor model performance against adversarial behaviors, contribute to fraud prevention strategy
Seniority
Senior, hands-on IC
