Data Scientist I

Role Overview

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. 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.

Responsibilities

• 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.

Requirements

• 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 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.
• Bachelor or Master's Degree in Computer Science or equivalent

Skills

• Python
• PyTorch
• TensorFlow
• AWS
• Azure