Applied Scientist, AWS Payments and Fraud Prevention

What you'll do

• Build and deploy fraud detection models and analyze behavioral, transactional, and historical data to identify patterns and emerging threats.
• Explore GenAI methods, including LLMs, synthetic data generation, and adversarial simulations, to strengthen fraud detection.
• Collaborate with partner teams, test and improve detection strategies, and monitor model performance against evolving fraud tactics.

What they ask for

• 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.
• Experience programming in Java, C++, Python or related language.
• Experience with deep learning model architecture, training, optimization, and pruning; and in at least one listed area: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high-performance computing.

Nice to have

• 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.

Pay

142,800.00 - 193,200.00 USD annually (base salary)

Tools

Java, C++, Python, GenAI, large language models (LLMs)

AI focus

Applies machine learning and GenAI—including LLMs, synthetic data generation, and adversarial simulations—to uncover fraud patterns and improve detection and defenses.