Visa is seeking to lead strategic initiatives and shape the future of fraud prevention at Visa by developing advanced risk detection and fraud prevention solutions that protect Visa and its clients globally.
Requirements
- Deep expertise in ML/DL model development, feature engineering, A/B testing, model deployment, and lifecycle management
- Strong foundation in algorithms, data structures, and problem-solving
- Experience building mission-critical systems that are secure, reliable, and performant
- Proven leadership in delivering real-world AI/ML products at scale, especially in high-stakes or regulated environments
- Experience with machine learning and deep learning models
- Experience with AI/ML
- Experience with software engineering
Responsibilities
- 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.
- 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.
Other
- 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.
- 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 position requires travel 5-10% of the time.
