Senior Director, Risk Data Science

PayPal•San Jose, CA

About The Position

Define and drive the strategic vision for machine learning initiatives at the organizational level. Lead the development and optimization of state-of-the-art machine learning models. Oversee the preprocessing and analysis of large datasets. Deploy and maintain ML solutions in production environments. Collaborate with cross-functional teams to integrate ML models into products and services. Monitor and evaluate the performance of deployed models, making necessary adjustments. Mentor and guide junior engineers and data scientists. Publish research findings and contribute to industry discussions. Represent the company at conferences and external engagements. Influence the direction of the company's AI/ML strategy and contribute to long-term planning. Build, mentor, and inspire a high-performing global team of Principal Data Scientists and Managers. Partner with the VP of Risk Data Science & AI to define and execute the long-term AI/ML roadmap for Global Fraud Prevention, moving beyond iterative improvements to transformative leaps in detection capability. Champion the adoption of advanced methodologies—specifically Graph Neural Networks (GNNs), Deep Learning, Reinforcement Learning, and GenAI-driven anomaly detection—to identify bad actors within our complex ecosystem of two-sided networks. Spearhead innovations and transformations for modeling algorithms, technical infrastructure, and modeling platforms to help propel business forward with the power of ML/AI World-Class Modeling & Execution End-to-End Ownership: Oversee the full lifecycle of mission-critical fraud models: from ideation and feature engineering (leveraging thousands of signals) to real-time production deployment and automated

Requirements

12+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience.
5 years of experience leading others
Extensive, hands-on experience building and managing fraud prevention and / or fraud detection models using large-scale data and advanced ML and AI techniques
18+ Years of Technical Experience: Proven track record of building and mentoring expert AI/ML teams that consistently deliver innovative solutions, resulting in measurable business impact and sustained competitive advantage
5+ Years of Executive Leadership: Experience managing large, multi-layered data science teams (managing managers) and influencing VP/C-level stakeholders.
Fintech & Payments DNA: Deep familiarity with the payments ecosystem (issuing, acquiring, gateways, ACH/Rails) and the unique adversarial nature of financial fraud.

Nice To Haves

Advanced Degree: Ph.D. in Computer Science, Statistics, Mathematics, Physics, Operations Research, or a related quantitative field is highly preferred.
Applied AI Scale: Experience deploying GNNs or Transformer-based models in a high-throughput, low latency production environment (not just offline research).
Transformation Experience: A track record of modernizing legacy model stacks (e.g., moving from logistic regression/forests to Deep Learning/AI) in a large enterprise.

Responsibilities

Define and drive the strategic vision for machine learning initiatives at the organizational level.
Lead the development and optimization of state-of-the-art machine learning models.
Oversee the preprocessing and analysis of large datasets.
Deploy and maintain ML solutions in production environments.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models, making necessary adjustments.
Mentor and guide junior engineers and data scientists.
Publish research findings and contribute to industry discussions.
Represent the company at conferences and external engagements.
Influence the direction of the company's AI/ML strategy and contribute to long-term planning.
Build, mentor, and inspire a high-performing global team of Principal Data Scientists and Managers.
Partner with the VP of Risk Data Science & AI to define and execute the long-term AI/ML roadmap for Global Fraud Prevention, moving beyond iterative improvements to transformative leaps in detection capability.
Champion the adoption of advanced methodologies—specifically Graph Neural Networks (GNNs), Deep Learning, Reinforcement Learning, and GenAI-driven anomaly detection—to identify bad actors within our complex ecosystem of two-sided networks.
Spearhead innovations and transformations for modeling algorithms, technical infrastructure, and modeling platforms to help propel business forward with the power of ML/AI
Oversee the full lifecycle of mission-critical fraud models: from ideation and feature engineering (leveraging thousands of signals) to real-time production deployment and automated