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 ownership of complex, enterprise scale portfolios rather than isolated programs • Proven ability to design, evolve, and sustain learning for experienced technical practitioners, not just early career or general audiences • Direct exposure to AI, ML, data, or engineering environments, with enough depth to understand real workflows, constraints, and trade offs • Demonstrated success operating in complex, global, matrixed organizations, where influence depends on alignment rather than authority • Track record of influencing senior stakeholders across Technology, Data, AI, and HR functions, including leaders with deeply held technical opinions • Ability to hold and enforce high standards while maintaining productive partnerships with engineering and platform leaders • Comfortable moving between strategic definition and hands on execution, making clear prioritization and scope decisions • Sufficient technical credibility to ask informed questions, challenge assumptions, and recognize when learning is disconnected from real practice • Clear, direct communicator who can engage senior technologists and executives without oversimplifying or posturing • Bias toward precision, rigor, and usefulness over generic frameworks, trends, or vendor led abstractions In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations. Pay Ranges Purchase, New York: $175,000 - $281,000 USD New York City, New York: $182,000 - $293,000 USD