Data Scientist I

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 Data Scientist I 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.

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 • 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 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 • Has strong knowledge 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. • Exposure to 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). • Exposure to MLOps practices, including model versioning, CI/CD for ML, and monitoring in production. • Exposure to cloud platforms (AWS, Azure) and data engineering tools like Databricks. • Solid understanding of prompt engineering, fine-tuning, and model