What are the responsibilities and job description for the Director of Machine Learning (Hybrid) position at Visa?
The payments industry is undergoing rapid transformation, with constant innovation and a growing demand for secure, intelligent, and scalable solutions. As a global leader, Visa is at the forefront of this evolution—aggressively investing in cutting-edge technologies to expand our capabilities and redefine what’s possible in the payments space.
If you're passionate about innovation, eager to make an impact, and thrive in a fast-paced environment, the Ecosystem & Operational Risk (EOR) Technology group within Visa’s Value-Added Services (VAS) business is the place to be.
Our Payment Fraud Disruption team plays a critical role in developing advanced risk detection and fraud prevention solutions that protect Visa and our clients globally. From concept to production, we build secure, scalable, and high-performance applications that safeguard billions of transactions.
We are currently seeking a Director of Machine Learning to lead several strategic initiatives and shape the future of fraud prevention at Visa.
Essential Functions:
- Lead the end-to-end development and deployment of machine learning and deep learning models—including data acquisition, feature engineering, experimentation, model refresh, and production monitoring.
- Drive the design and implementation of secure, reliable, and scalable ML systems for real-time fraud detection and risk mitigation.
- Partner with Product Management and Engineering teams to define AI/ML strategies, translate business problems into ML solutions, and align on priorities and roadmaps.
- Oversee model performance tracking and lead continuous improvement efforts through rigorous evaluation and iteration.
- Guide architecture and design decisions, incorporating the latest advancements and best practices in AI/ML and software engineering.
- Lead and manage cross-functional Agile teams, ensuring the delivery of impactful and high-quality features.
- Champion a culture of operational excellence by implementing tools, workflows, and best practices that drive productivity and reduce development cycle times.
- Establish and track engineering effectiveness, product quality, and delivery performance metrics—and guide the team to exceed them.
- Stay up to date with the latest developments in AI/ML and help shape the long-term technical strategy.
- Mentor and grow a geographically distributed team of machine learning engineers, data scientists, and software engineers.
- Play a key role in talent acquisition, coaching, and leadership development across the team.
This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.