Director, AI & Advanced Data Learning & Development

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Director, AI & Advanced Data Learning & Development

Role Summary

At Mastercard, AI and data systems are core to how our platforms operate, how decisions are made, and how risk is managed. The engineers and data scientists who design and run these systems require continuous, high quality skill development that keeps pace with how the work is actually done in production.

The Director, AI & Advanced Data Learning is responsible for building and sustaining deep, practitioner level learning for Mastercard’s most technical roles, including AI engineers, machine learning engineers, data scientists, and emerging specialist roles. This role is not focused on general AI literacy or enterprise wide adoption. It is deliberately scoped to advanced technical practice.

Reporting to the VP, Data & Technology Learning, this role designs learning aligned to real tools, platforms, workflows, and constraints that technical teams face when building and operating AI and data systems at scale.

Key Responsibilities

Set and Own the Advanced AI & Data Learning Agenda
• Own the end to end advanced learning strategy for AI engineers, ML engineers, data scientists, and emerging specialist roles, aligned to Mastercard’s AI and data platform direction
• Translate enterprise AI strategy and platform roadmaps into clear skill priorities, learning investments, and sequencing decisions
• Continuously reassess priorities as tools, platforms, and practices evolve, retiring content and approaches that no longer reflect how work is done

Enable Progression Across Defined Proficiency Levels
• Use existing role based skills and proficiency standards as the foundation, focusing on how practitioners move from one level to the next
• Design practical progression mechanisms—learning, practice, and experiences—that help people close the most common gaps between proficiency levels in real work contexts
• Partner with senior AI, data, and engineering leaders to validate that progressions reflect real performance differences, and continuously refine approaches based on observed outcomes

Design and Deliver Production Relevant Learning
• Build learning grounded in real systems and workflows, including:

o Model development, evaluation, and iteration
o Data and feature pipelines
o Deployment, monitoring, and lifecycle management
o MLOps / LLMOps, reliability, performance, and cost considerations
o Responsible AI, governance, and risk controls as they show up in practice

• Prioritize hands on learning approaches (labs, platform scenarios, real failure modes) over abstract content
• Ensure learning complements how teams actually ship, debug, and maintain AI and data systems

Lead Through Influence in a Matrixed Organization
• Act as a senior learning leader who works cross functionally and without direct authority across Technology, Data, AI, and HR ecosystems
• Navigate competing priorities and viewpoints, shaping decisions through credibility and judgment rather than position
• Serve as a trusted partner to senior technologists, holding a clear point of view while building durable relationships

Portfolio, Investment, and Partner Management
• Own a focused portfolio of advanced AI and data learning initiatives with clear accountability for outcomes
• Make explicit trade offs on depth, breadth, and scale based on business impact, not participation metrics
• Evaluate, select, and govern external partners and vendors, holding a high bar for technical depth, relevance, and production realism

Measure Impact and Continuously Improve
• Define success using indicators that matter to technical leaders, such as:

o Speed to production readiness
o Reduction in repeat defects or rework
o Consistency in how models are built, deployed, and governed

• Establish feedback loops with engineering and platform leaders to validate whether learning is improving real performance
• Use insights to continuously adapt strategy, content, and delivery models


Experience & Capabilities

• Significant experience in Learning & Development, talent development, or capability development, with own