Vice President, Data Scientist, Open Finance

Mastercard

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: New York, NYPurchase, NY
Salary: $244,000–$390,000 / yr
Posted: 1 day ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $200k

This role $317k

$121k $419k

below market most similar roles pay here above market

This role pays more than 93% of similar roles. Most pay $165,000–$234,250 — the blue band above. At the midpoint, this role pays about $317k versus about $200k for comparable roles.

Based on 240 similar postings.

Employer

About Mastercard

Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services

Mastercard currently has 152 open roles on FindRole.

Listed pay typically runs $122,000–$207,000 across 134 roles with salary data.

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At a glance

TL;DR · Vice President, Data Scientist, Open Finance

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JOB TITLE: Vice President, Data Scientist (Open Finance) The Vice President, Data Scientist (Open Finance) leads a global organization dedicated to developing advanced data science and AI capabilities for the Open Finance business. This leader builds and scales a team focused on transforming financial data into actionable insights, predictions, and decisioning capabilities. Key responsibilities include defining the data science strategy, establishing a scalable roadmap, and overseeing the development of production-grade models for transaction categorization, entity extraction, fraud detection, credit risk, and propensity modeling. The role requires expertise in machine learning, generative AI, foundation models, LLMs, deep learning, NLP, and MLOps. The position solves complex business problems by translating customer needs into scalable solutions, ensuring models are accurate, explainable, and compliant with regulatory, privacy, and responsible AI requirements within the financial services and payments domain.

What does a Data Scientist earn in New York?

Median $173200 from 42 postings across 19 companies.

See salary data

Skills

Machine Learning Generative AI LLMs NLP Deep Learning MLOps Predictive Analytics Data Science Model Governance Responsible AI Entity Extraction Fraud Detection

What you'll do

Define and execute the data science strategy for Open Finance to align with business and product objectives.
Build and scale a high-performing global data science organization with clear technical standards and career frameworks.
Lead the development of production-grade models for transaction categorization, fraud detection, credit risk, and propensity modeling.
Translate complex business problems into scalable data science solutions in partnership with Product, Engineering, and Sales.
Establish rigorous standards for model governance, including validation, monitoring, explainability, and responsible AI requirements.
Implement MLOps practices to ensure models are accurate, scalable, and continuously improved in production environments.
Evaluate and adopt emerging technologies like generative AI, LLMs, and deep learning to create measurable commercial value.

What we're looking for

Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Engineering, Data Science, or a related quantitative discipline.
Advanced degree in a quantitative discipline or equivalent work experience (preferred).
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 applied to real-world business problems.
History of 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.