Director, Machine Learning

Company Description

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience Life 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, 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.

Visa is not offering relocation assistance for this role.

Basic Qualifications:

• 10+ years of relevant work experience with a Bachelor’s degree, OR
• 13+ years of relevant experience in lieu of a degree

Preferred Qualifications:

• 12+ years of experience with a Bachelor’s degree, OR
• 8–10 years with an Advanced Degree (e.g., MS, MBA), OR
• 6+ years with a PhD in Computer Science, Data Science, Statistics, or a related field
• Proven leadership in delivering real‑world AI/ML products at scale, especially in high‑stakes or regulated environments
• 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
• Demonstrated success in driving Agile delivery, improving engineering metrics, and fostering…