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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, Project Management (AI) The Strategy, Enablement & Transformation team is composed of strong contributors with deep project and program management expertise, leadership capability, and a consulting mindset. The Project Management as a Service organization within the Global Business Solutions Center focuses on delivering complex, cross-functional transformation initiatives across Finance, Marketing, GBSC, P&C, LF&I, and other key enterprise functions.
This Director, Project Management role will lead project management for AI initiatives across the Finance organization. The role will be responsible for bringing structure, governance, and delivery rigor to a growing portfolio of AI opportunities, helping Finance accelerate practical adoption through disciplined intake, prioritization, execution, responsible AI alignment, and measurable value realization.
The qualified candidate will be a member of a highly visible team working closely with Finance leadership, GBSC stakeholders, AI and automation partners, technology teams, risk and governance partners, and executive sponsors to identify opportunities, shape execution plans, manage dependencies, and drive outcomes. This role requires a highly collaborative, strategic, and execution-oriented leader who can operate in ambiguity, simplify complexity, influence across senior stakeholder groups, and maintain momentum across multiple workstreams.
The ideal candidate will bring strong program governance, executive communication, stakeholder management, and delivery leadership skills. They must be able to establish operating rhythms, drive clarity around decision rights and escalation paths, translate complex program details into executive-ready updates, and ensure AI initiatives are progressing with clear scope, milestones, risks, benefits, and measurable outcomes.
Role:
- Lead project and program management for Finance AI initiatives, including AI literacy, idea intake and prioritization, quick-win use cases, priority pilots, and value realization tracking.
- Establish and manage program governance structures, including steering committee routines, working team cadences, decision logs, RAID management, escalation paths, and executive reporting standards.
- Partner with Finance leadership and executive sponsors to translate strategic priorities into executable roadmaps, delivery plans, milestones, and measurable outcomes.
- Coordinate across Finance, GBSC Tech, AI / Automation, AI CoE, AI Governance, Risk, Data, Controls, and other partners to ensure initiatives are appropriately shaped, governed, and executed.
- Develop, run, and distribute executive-ready status updates, dashboards, decision materials, and progress reports that clearly communicate scope, progress, risks, dependencies, decisions needed, and business value.
- Drive cross-functional alignment across complex stakeholder groups, proactively identifying blockers, managing tradeoffs, resolving issues, and keeping workstreams moving toward agreed outcomes.
- Support disciplined use case intake, prioritization, pilot planning, benefits tracking, and scale / stop decisions to ensure AI opportunities are aligned to Finance priorities and responsible AI expectations.
- Provide insights to enhance processes, tooling, governance routines, and delivery practices, with a focus on scalable operating models and continuous improvement.
- Perform analysis and assessment activities to provide stakeholders with timely updates, actionable recommendations, and clear visibility into program health.
- Regularly communicate with senior and executive leadership regarding program status, risks, decisions, dependencies, and progress against expected outcomes.
All About You:
- Experience leading large-scale, cross-functional transformation programs in complex, matrixed organizations.
- Strong project and program management experience, with demonstrated ability to manage multiple workstreams, executive stakeholders, dependencies, risks, and delivery milestones.
- Proven ability to establish program governance, operating rhythms, decision forums, escalation paths, status reporting, and executive-level communications.
- Excellent executive communication skills, including the ability to synthesize complex information into concise, data-backed, visually clear narratives for senior leadership.
- Demonstrated ability to operate in ambiguity, bring structure to complex initiatives, manage competing priorities, and drive results through influence rather than authority.
- Experience working on AI, automation, data, digital transformation, finance transformation, or technology-enabled business change initiatives preferred.
- Strong stakeholder management skills, with the ability to build trusted partnerships across Finance, Technology, Risk, Governance, Operations, and senior leadership teams.
- Experience evaluating business processes and advising on ways to streamline operations, improve controls, increase efficiency, and enable scalable ways of working.
- Strong understanding of project risk management, issue resolution, business process assessment, benefits tracking, and change management.
- Expert presenter using data, visuals, and storytelling to communicate project status, risks, decisions, and value to various audiences, including executive stakeholders.
- Professional services, consulting, PMO, transformation office, or enterprise program leadership experience a plus.
- Experience analyzing operational or program data to support business decisions and drive continuous improvement, including process design, system optimization, and measurable value realization.
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.Pay Ranges
O'Fallon, Missouri: $143,000 - $236,000 USD Purchase, New York: $164,000 - $271,000 USD
Salary Context
This $143K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($189K) sits 11% below the category median. Disclosed range: $143K to $236K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Mastercard AI Hiring
Mastercard has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Gurugram, Haryana, India, New York, NY, Purchase, NY. Compensation range: $236K - $236K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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