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
Senior Data Scientist
Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Overview
Global Risk and Compliance contributes towards the risk and compliance portfolios of Mastercard. Compliance program works towards Transaction Monitoring in the fields of Anti Money Laundering, Regulatory compliance requirements etc. We are looking at implementing an AI based solution to support increased transaction monitoring for AML activities. If you are the one who enjoys solving problems in a challenging environment, and who has the desire to take their career to the next level. If any of these opportunities excite you, we would love to talk.
Role
• Design and implement generative AI models and applications for AML transaction monitoring use cases.• Build advanced solutions leveraging Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), tool calling, and LangChain/LangGraph for dynamic workflows.• Develop and optimize machine learning pipelines for fine-tuning large language models (LLMs) and other foundation models.• Implement content generation systems for text, image, and multimodal outputs.• Apply computer vision techniques for image analysis, creative generation, and visual search.• Implement evaluation frameworks for GenAI applications, including hallucination detection, bias checks, and quality scoring. • Build observability and monitoring solutions for AI systems, including latency, cost tracking, and model performance metrics.• Build scalable solutions on cloud platforms (AWS, Azure) and leverage data platforms like Databricks.• Integrate AI models into production systems with a focus on performance, security, and compliance.• Stay current with the latest advancements in AI/ML research, particularly in generative AI, and apply them to real-world problems.• Promote engineering best practices and contribute to a culture of innovation and collaboration.
All About You
• Hands on experience of 2-3 years in implementing LLM models. • Strong hands-on experience in building and deploying generative AI solutions (e.g., LLMs, diffusion models, transformers). • Expertise in RAG pipelines, MCP-based integrations, tool calling frameworks, and LangChain/LangGraph. • Proficiency in Python and popular AI/ML frameworks such as PyTorch or TensorFlow. • Experience with content generation systems (text, image, multimodal) and computer vision models. • Experience with evaluation techniques for GenAI (e.g., hallucination detection, factuality scoring, bias evaluation). • Knowledge of observability tools for AI systems (e.g., monitoring latency, cost, and performance metrics). • Experience with MLOps practices, including model versioning, CI/CD for ML, and monitoring in production. • Experience with cloud platforms (AWS, Azure) and data engineering tools like Databricks. • Solid understanding of prompt engineering, fine-tuning, and model evaluation techniques. • Knowledge of API development and integration of AI models into web or mobile applications. • Strong problem-solving skills and ability to work in a fast-paced, high-impact environment. • Excellent communication and collaboration skills to work effectively with technical and non-technical stakeholders.
Education: • Bachelor or Master's Degree in Computer Science or equivalent
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.
Skills Required
- 2–3 years of hands-on experience implementing LLM models
- Experience building and deploying generative AI solutions using LLMs, diffusion models, or transformers
- Expertise in RAG pipelines, MCP integrations, tool-calling frameworks, and LangChain or LangGraph
- Proficiency in Python and AI/ML frameworks such as PyTorch or TensorFlow
- Experience with text, image, and multimodal content generation systems
- Experience with computer vision models
- Experience evaluating generative AI systems, including hallucination detection, factuality scoring, and bias evaluation
- Knowledge of AI observability, including latency, cost, and performance monitoring
- Experience with MLOps, model versioning, machine learning CI/CD, and production monitoring
- Experience with AWS or Azure and data engineering tools such as Databricks
- Understanding of prompt engineering, fine-tuning, and model evaluation
- Knowledge of API development and AI model integration into web or mobile applications
- Bachelor’s or master’s degree in Computer Science or equivalent
- Strong problem-solving, communication, and collaboration skills
Mastercard Compensation & Benefits Highlights
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Retirement Support — Retirement plans are presented as best-in-class with a high company match on 401(k) or local equivalents. Career materials and U.S. postings consistently highlight retirement matching as a standout feature.
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Leave & Time Off Breadth — U.S. postings describe generous paid time off including vacation, personal days, holidays, sick/safe time, and additional bereavement leave. A hybrid policy and a limited “work from elsewhere” option further support time away.
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Parental & Family Support — Company pages state a global minimum of 16 weeks of paid new-parent leave across birth, adoption, and foster, plus family-building assistance where permitted. Mental-health resources and caregiving supports are also emphasized.
Mastercard Insights
What We Do
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient 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.
Why Work With Us
We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.






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