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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
Vice President, AI \& Data Strategy – Open Finance
Overview
The AI and Data Office is responsible for the strategy, acquisition, evaluation, and responsible use of AI and data capabilities that power innovation and operations across Mastercard. Reporting to the Senior Vice President, AI \& Data Strategy, the Vice President will partner with Open Finance and broader Services leaders to define and execute AI and data strategies that accelerate product innovation, strengthen decision\-making, and enable scalable growth across a complex, regulated ecosystem.
This role sits at the intersection of product strategy, data governance, and AI enablement—translating business priorities into actionable AI and data initiatives, addressing capability and regulatory gaps, and enabling responsible adoption across Open Finance products, platforms, and partnerships. The role requires deep domain expertise across payments, Open Banking/Open Finance, and data ecosystems, combined with strong strategic judgment and executive influence.
Role Responsibilities:
- Define and drive the AI and data strategy roadmap across Open Finance and Services, ensuring alignment to product priorities, regulatory requirements, and measurable business outcomes.
- Partner with business, product, and technology leaders to translate Open Finance priorities into AI and data initiatives while identifying and addressing gaps in data capabilities, governance, and operating models.
- Serve as a strategic advisor on internal and third\-party data and AI capabilities, guiding evaluation, prioritization, and implementation of high\-impact solutions across Open Finance use cases.
- Drive the application of AI and data across Open Finance products, including customer data, transaction data, and external data sources, to enhance product capabilities, risk management, and customer outcomes.
- Lead adoption of data and AI design practices across products and enterprise initiatives, including data requirements, AI intake, and governance processes aligned with regulatory and compliance standards.
- Ensure alignment with regulatory, data privacy, and compliance requirements, including Open Banking frameworks, data sharing standards, and evolving global regulatory expectations.
- Partner closely with data governance, privacy engineering, and risk teams to enable responsible AI adoption and ensure appropriate data usage, controls, and safeguards.
- Build strong relationships across Open Finance, Services, and enterprise stakeholders to champion AI and data best practices and strengthen organizational readiness.
- Represent Mastercard externally as a senior voice in Open Finance and AI, engaging with clients, partners, and industry stakeholders to advance Mastercard’s strategy and reputation.
- Define, track, and communicate success metrics, including AI adoption, product impact, and other indicators of strategy effectiveness and business value.
- Foster a culture of innovation, accountability, operational ownership, and execution excellence across cross\-functional teams.
All About You:
- Proven experience defining and executing AI and data strategies in product\-led, highly regulated environments, preferably within payments, fintech, or Open Finance ecosystems.
- Deep understanding of Open Finance and Open Banking, including card and A2A payment systems, data flows, external data sources, and customer data ecosystems.
- Strong knowledge of regulatory, compliance, and data governance requirements, including data privacy, risk management, and responsible AI practices.
- Experience partnering with data governance, privacy engineering, and compliance teams to enable secure and responsible use of data and AI.
- Strong understanding of AI, data, and analytics, with the ability to connect technical capabilities to product strategy, commercial outcomes, and customer value.
- Exceptional executive communication and stakeholder management skills, with the ability to influence C\-level leaders across product, technology, and business organizations globally.
- Strong business and financial acumen, with experience prioritizing investments and driving cost\-efficient, scalable solutions.
- Proven ability to lead cross\-functional initiatives and build strong relationships across diverse stakeholder groups in complex, matrixed environments.
- Ability to shape practical strategies and operating models that define how AI and data are governed, prioritized, and applied across products and platforms.
- Experience working with complex data ecosystems and multiple data sources, including internal, partner, and third\-party data environments.
- Comfortable operating in fast\-paced, highly dynamic environments with significant complexity, ambiguity, and executive visibility.
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 reasonable\[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
Purchase, New York: $235,000 \- $375,000 USD
Arlington, Virginia: $235,000 \- $375,000 USD
New York City, New York: $245,000 \- $391,000 USD
O'Fallon, Missouri: $204,000 \- $326,000 USD
Salary Context
This $204K-$375K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($289K) sits 60% above the category median. Disclosed range: $204K to $375K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Mastercard AI Hiring
Mastercard has 6 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Arlington, VA, US, O'Fallon, MO, US, Harrison, NY, US. Compensation range: $169K - $375K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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