Tech Stack

Description

You will guide the design and delivery of real-time ML models that protect our global payment network against fraud. You'll work at the intersection of machine learning, graph-based detection, and behavioral modeling, collaborating with engineers, fraud analysts, and product managers to turn research into production systems that reduce false positives and keep legitimate customers secure.

Requirements

• Degree or PhD in STEM discipline, or equivalent commercial experience
• Hands-on experience with deep learning, graph-based methods, or sequence models
• Track record of deploying models in production that create measurable value
• Ability to translate complex ML concepts into clear solutions for stakeholders
• Experience writing clean code and contributing through code reviews

Responsibilities

• Contribute to full model lifecycle from discovery to production, experimentation, and monitoring
• Design and refine real-time ML systems to reduce false positives while ensuring secure experience
• Foster responsive feedback loop to adapt models as fraud patterns evolve
• Help shape technical direction of fraud prevention ML as we expand globally
• Support team growth through technical mentorship and knowledge sharing