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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 & Data Strategy Role Summary
The Director, AI & Data Strategy is an established subject-matter expert responsible for advancing Mastercard’s thinking, frameworks and practices across AI and data strategy. The role applies deep expertise and extensive experience to deliver key strategic initiatives and make significant improvements to new and existing products, services and business processes.
The Director leads pioneering research, evaluates emerging AI and data developments, and provides forward-looking guidance on their potential business, regulatory, ethical and societal implications. Working across business, technology, legal, privacy, compliance and risk teams, the role develops responsible AI frameworks, anticipatory guardrails and practical methods that enable innovation while protecting customer trust and Mastercard’s long-term resilience.
Key Responsibilities:
- AI and Analytics Integration: Champion the responsible incorporation of advanced analytics, generative AI, agentic AI and other innovative methodologies into core products, services, processes and decision-making frameworks.
- Cross-Functional Governance Partnership: Collaborate with legal, privacy, compliance, risk, technology, data, product and business teams to embed responsible AI and data practices into operational workflows, product-development processes and strategic decisions.
- Global Standards and Societal Expectations: Monitor evolving regulatory requirements, industry standards, ethical expectations and societal concerns. Help Mastercard’s frameworks and practices remain responsive to changing global expectations.
- Data and AI Literacy: Promote a culture of data literacy, responsible experimentation and informed decision-making. Develop practical guidance that enables technical and non-technical colleagues to understand both the potential and limitations of AI and data-driven approaches.
- Methods and Best Practices: Create reusable frameworks, assessment models, research methods, decision tools and best practices that improve the consistency and quality of AI and data strategy across Mastercard.
- Capability Development and Mentoring: Mentor colleagues by sharing specialist knowledge, emerging research, innovative techniques and best practices. Build deeper expertise within the team and strengthen Mastercard’s broader community of AI and data practitioners.
All About You:
- Recognized subject-matter expertise in AI strategy, data strategy, responsible AI, AI governance, technology policy or a closely related discipline.
- Significant experience applying specialist knowledge to complex business, technology, regulatory or societal challenges.
- Deep understanding of generative AI, agentic AI, machine learning, advanced analytics and the data foundations required to support these capabilities.
- Demonstrated ability to lead research and horizon-scanning activities and translate emerging developments into strategic business implications.
- Experience developing advanced governance frameworks, ethical standards, guardrails, assessment models or responsible innovation practices.
- Ability to evaluate emerging and hypothetical risks where historical evidence, regulatory direction or established industry practices may be limited.
- Strong understanding of privacy, security, transparency, fairness, accountability, data rights, model risk and other responsible AI considerations.
- Ability to communicate complex and uncertain topics clearly to senior executives, technical specialists, control functions and business stakeholders.
- Demonstrated influence across organisational boundaries, particularly when working with legal, compliance, privacy, risk, technology and product teams.
- Strong critical-thinking skills, intellectual curiosity and willingness to challenge assumptions constructively.
- Ability to balance innovation, customer value, feasibility, governance and long-term societal considerations.
- Experience mentoring colleagues and developing specialist capability through knowledge sharing and practical guidance.
- Bachelor’s degree in data science, computer science, engineering, technology, public policy, law, ethics, business or equivalent practical experience. An advanced degree is advantageous but not required.
Preferred Experience
- Experience within payments, financial services or another highly regulated and globally distributed industry.
- Experience in responsible AI, AI assurance, technology ethics, algorithmic accountability, model governance or technology-policy research.
- Familiarity with global AI regulations, governance standards and industry frameworks.
- Experience advising senior leaders on emerging technologies, strategic uncertainty and long-term organisational resilience.
- Evidence of external or internal thought leadership through research papers, executive publications, conference participation or industry working groups.
- Experience translating research and policy developments into operational controls, product-development practices and enterprise governance.
Measures of Success
- Mastercard has a clear, credible and forward-looking perspective on emerging AI and data developments.
- Senior leaders receive timely and actionable guidance on emerging opportunities, risks, ethical considerations and potential societal impacts.
- Responsible AI frameworks and guardrails anticipate future developments rather than addressing only known or immediate risks.
- Emerging AI capabilities are evaluated consistently against customer value, technical feasibility, strategic relevance, ethical considerations and potential harm.
- Responsible AI and data practices are embedded into product, technology and operational workflows across Mastercard.
- Complex research and emerging trends are translated into practical frameworks, decisions and organisational actions.
- AI and data literacy improves across technical, business and control functions.
- Reusable research methods, governance models and best practices strengthen the consistency and maturity of Mastercard’s AI and data strategy.
- Team members and cross-functional partners develop deeper expertise through mentoring, knowledge sharing and exposure to emerging practices.
This role is not eligible for Mastercard-sponsored work authorization. Candidates must be legally authorized to work in the United States now and in the future without employer sponsorship. 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 [email protected] 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.THIS POSTING IS NOT FOR A CURRENT VACANCY, BUT MASTERCARD IS SEEKING RESUMES TO REVIEW IN THE FUTURE WHEN JOBS BECOME AVAILABLE.Pay Ranges
Purchase, New York: $179,000 - $305,000 USD O'Fallon, Missouri: $156,000 - $265,000 USD
Salary Context
This $156K-$305K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mastercard, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($230K) sits 9% above the category median. Disclosed range: $156K to $305K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mastercard AI Hiring
Mastercard has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Harrison, NY, US, O'Fallon, MO, US, Pune Division, Maharashtra, India. Compensation range: $147K - $407K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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