Senior Director, Risk Data Science

Strategize and lead organizational ML initiatives to transform fraud detection into proactive, autonomous prevention.
Build and direct a global team of ~75 data scientists, fostering rigorous scientific inquiry and rapid experimentation.
Own end-to-end fraud model lifecycle—from ideation and feature engineering across thousands of signals to real-time production deployment and automated retraining.
Incorporate Graph Neural Networks, Deep Learning, Reinforcement Learning, and GenAI-driven anomaly detection into high-throughput, low-latency decision engines.
Collaborate with product, engineering, and risk leadership to embed Safety and Security as core features in launches across the ecosystem.
Promote thought leadership through research publications and conference representation; define the long-term AI/ML roadmap for Global Fraud Prevention.