Skills this job asks for
Machine learningLeadershipLLMsDeep learningA/B testingSparkCI/CD
Add your CV (free) to see which of these your CV is missing.
Sponsorship verdict
No sponsorship evidence yet
No government record and no wording either way. Not a refusal: ask the recruiter.
-
?
No government sponsor record hereNo government sponsor record covers this employer in this country.
-
?
The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
-
?
Can’t check pay against the visa rulesWe don’t have visa salary rules for this country yet.
-
✓
Confirmed live todayWhen a source last listed this job as open.
Details and sources
A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.
Before you apply
Sponsorship unknown
No register record and no sponsorship wording in the posting. Worth asking the employer before investing significant time.
These come from the posting’s own wording. Read the original listing to confirm. See better-fit alternatives →
About the role
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
Lead Data Scientist
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.
Overview
The Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s fraud and risk product, the team builds and enhances models that analyze billions of transactions and enable real-time detection of fraudulent activity.
We are looking for a highly motivated Lead Data Scientist to drive the next generation of AI-powered fraud detection systems. This is a senior, hands-on technical role that combines deep individual contribution with technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning systems, and large-scale data engineering while contributing directly to Mastercard's mission of enabling secure and trusted commerce worldwide.
Role
As a Lead Data Scientist, you will be expected to be highly hands-on while providing technical leadership in designing, developing, coding, testing, and deploying enterprise-scale AI solutions that enhance fraud detection and risk intelligence capabilities across Mastercard's Security Solutions portfolio. This is not just a model development role; it is a strong fit for someone who enjoys working closely with complex data, exploring patterns, understanding the business and technical problem deeply, experimenting through R&D, and bringing interdisciplinary research ideas—such as advances in NLP, deep learning architectures, graph learning, or agentic systems —into the fraud and security space. The role is also about identifying practical solutions and implementing them in scalable production systems.
You will lead complex AI engineering initiatives while directly contributing to model development, experimentation, system design, and production implementation.
Key responsibilities include:
Lead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
Define, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement.
Hands-on design and implementation of Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.
Work with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.
Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frame
Apply on employer site ↗