Director, AI & Advanced Data Learning & Development

Mastercard•Purchase, NY

1d•$175,000 - $293,000•Remote

About The Position

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.

Requirements

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

Responsibilities

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
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
Build learning grounded in real systems and workflows, including: Model development, evaluation, and iteration; Data and feature pipelines; Deployment, monitoring, and lifecycle management; MLOps / LLMOps, reliability, performance, and cost considerations; 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
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
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
Define success using indicators that matter to technical leaders, such as: Speed to production readiness; Reduction in repeat defects or rework; 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

Benefits

insurance (including medical, prescription drug, dental, vision, disability, life insurance)
flexible spending account and health savings account
16 weeks of new parent leave
up to 20 days of bereavement leave
80 hours of Paid Sick and Safe Time
25 days of vacation time
5 personal days
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