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:
oModel development, evaluation, and iteration
oData and feature pipelines
oDeployment, monitoring, and lifecycle management
oMLOps / LLMOps, reliability, performance, and cost considerations
oResponsible 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:
oSpeed to production readiness
oReduction in repeat defects or rework
oConsistency 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
Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.
Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard's security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
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
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