Senior Analyst​/Data Scientist; Compliance Strategy, Data Intelligence & Innovation

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.

Senior Analyst, Data Scientist - Compliance Strategy, Data Intelligence & Innovation

The Compliance Strategy, Data Intelligence & Innovation Group (CSDII) helps detect, investigate, and prevent financial crime across the transaction lifecycle. We work closely with internal Compliance, Legal, Technology, and Operations teams to strengthen anti-money laundering (AML), counter‑terrorist financing (CTF), and sanctions compliance by using data to obtain actionable insights aiming to continuously improve our network.

In the Sr. Analyst, Data Scientist role, you will support transaction and sanctions monitoring programs by performing ongoing tuning of alerts and measuring what works. CSDII is looking for someone who enjoys analyzing data to generate practical improvements, can simplify and automate manual work, and can support AI and analytics projects that strengthen monitoring outcomes in a well‑governed and well‑documented manner.

• Support tuning and optimization of sanctions screening and transaction monitoring scenarios, with a focus on alert quality, false positive reduction, and regulatory defensibility.
• Design, test, and validate scenario risk indicators and analytical features using transactional, entity, and behavioral data.
• Document insights across the scenario lifecycle, including hypothesis development, tuning analysis, validation, and production readiness.
• Monitor and assess scenario and model performance to ensure explainability, auditability, and regulator readiness.
• Prepare clear summaries of scenario performance, trade-offs, and residual risk for compliance leadership and partners.
• Build and maintain reusable Python and SQL code to pull, clean, and analyze monitoring data while assuring outputs are accurate, repeatable, and easy to audit.
• Look for ways to automate repeatable manual work (e.g., data preparation, recurring reports, and quality checks) and help test simple solutions with technology partners.
• Support AI and advanced analytics work (e.g., help create features, check how models perform over time, and summarize results in a way that is easy to understand).
• Turn data analysis into clear notes, visuals, and simple documentation that explains what was done, why, and what changed.
• Help improve data quality by reconciling key fields, flagging unusual patterns, and supporting root cause analysis to reduce noise in monitoring.
• Work with investigators and monitoring specialists to understand common typologies, regulatory expectations and reflect that context in your data analysis and automation work.

• Bachelor’s degree (or equivalent) in Data Science, Data Analytics, Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
• Strong foundation in data analysis, including descriptive statistics, basic hypothesis testing, and translating business questions into analytical approaches.
• Hands‑on experience with Python to support data analytics (e.g., pandas, Num Py, scikit‑learn).
• Working knowledge of SQL (joins, aggregations, window functions) and an understanding of how to validate and reconcile results.
• Experience with, or a strong understanding of, AI concepts and how they can be applied in a controlled way towards financial crime prevention (e.g., using machine learning or generative AI to help prioritize alerts, reduce false positives, or automate documentation and reporting).
• Demonstrated ability to reduce alert noise while maintaining or improving detection coverage through data‑driven analysis.
• Experience translating business or regulatory questions into analytical features, thresholds, or performance metrics.
• Experience…