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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, Data Scientist (Open Finance) Overview
We are seeking an experienced and visionary Vice President of Data Science – Open Finance to lead the development, and delivery of advanced data science and AI capabilities that power our Open Finance business.
This people leader will be responsible for building and scaling a world-class Data Science organization focused on transforming financial data into actionable insights, predictions, and decisioning capabilities. The role will span foundational data intelligence and high-value applications including transaction categorization, entity extraction, income and revenue classification, fraud detection, credit risk, propensity modeling, and other AI-powered financial insights.
The VP will work closely with their immediate team and senior leaders across Product, Engineering, Sales, Privacy and other business functions to identify high-value opportunities, translate business problems into data science solutions, and deliver capabilities that create measurable customer and commercial value.
The ideal candidate combines deep expertise in modern data science and AI with strong product acumen and a track record of scaling teams and production-grade machine learning solutions.
Role
Data Science Strategy
- Define and execute the Data Science strategy for Open Finance in alignment with overall business and product objectives.
- Identify opportunities where advanced analytics, machine learning, and AI can create differentiated products, improve decisioning, reduce risk, and drive growth.
- Establish a scalable roadmap spanning foundational intelligence capabilities and application-specific models and solutions.
- Evaluate and adopt the latest advances in machine learning, generative AI, foundation models, LLMs, deep learning, NLP, and other emerging technologies where they can create meaningful business value.
Data Science Products & Solutions
- Ensure models are accurate, scalable, explainable where required, resilient, and continuously improved based on performance and changing market conditions.
- Lead the development and continuous improvement of production-grade models and AI capabilities, including:
- Transaction categorization
- Entity extraction, resolution, and enrichment
- Income and Revenue classification
- Fraud detection and risk scoring
- Credit risk and credit decisioning
- Spend propensity and customer insights
- Personalization and predictive analytics
- New AI-powered capabilities that leverage Open Finance and other proprietary data assets
Cross-Functional Leadership
- Partner closely with Product, Engineering, Sales, Risk, Marketing, and business leaders to understand customer needs and translate them into scalable data science solutions.
- Work directly with Sales and customers to understand market requirements, demonstrate the value of AI-powered capabilities, and support strategic opportunities.
- Collaborate with Engineering to establish scalable production architectures, MLOps practices, model deployment, monitoring, and operational excellence.
- Serve as a senior thought leader and trusted advisor to executives and key stakeholders on the application of AI and data science to Open Finance.
Organizational Leadership
- Build, lead, and develop a high-performing global Data Science organization.
- Establish the appropriate organizational structure, technical standards, operating model, and career development framework for the team.
- Attract, retain, and develop world-class data science and AI talent.
- Foster a culture of innovation, experimentation, scientific rigor, collaboration, and accountability for business outcomes.
- Establish strong partnerships between Data Science, Product, and Engineering, with clear ownership and shared objectives.
Model Governance & Responsible AI
- Establish rigorous standards for model development, validation, monitoring, explainability, and performance.
- Ensure solutions meet applicable regulatory, privacy, security, fairness, and responsible AI requirements.
- Partner with Risk, Legal, Compliance, and other control functions to ensure appropriate governance for AI and machine learning solutions.
All About You
- Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Engineering, Data Science, or a related quantitative discipline; advanced degree preferred or equivalent work experience.
- Previous experience in data science, machine learning, AI, analytics, or a related field, with significant leadership experience.
- Proven experience leading and scaling large, high-performing Data Science or AI organizations.
- Deep understanding of modern machine learning and AI techniques and their application to real-world business problems.
- History taking machine learning models from research and experimentation through production, commercialization, and ongoing optimization.
- Background in financial services, fintech, payments, Open Banking/Open Finance, lending, fraud, or risk preferred.
- Strong understanding of modern data and AI platforms, MLOps, cloud technologies, and production machine learning environments.
- Strategic and pragmatic: able to balance long-term AI innovation with near-term execution.
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
New York City, New York: $244,000 - $390,000 USD Purchase, New York: $233,000 - $374,000 USD
Salary Context
This $233K-$390K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Mastercard, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. This role's midpoint ($311K) sits 64% above the category median. Disclosed range: $233K to $390K.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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