Pune, Mahārāshtra, India
Live, first seen by us 71 days agoImplement generative AI models and applications for AML transaction monitoring
Build RAG, MCP, and LangChain/LangGraph solutions for dynamic workflows
Develop ML pipelines for fine-tuning large language models
Implement evaluation frameworks for GenAI, including hallucination and bias checks
Build observability and monitoring for AI systems, including latency and cost
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 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.
• 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 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
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Day 71
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Well past Mastercard’s usual: its jobs come down by about day 25.
From 11,783 similar postings that came down, and 268 of Mastercard’s. Coming down is not always a hire.
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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 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
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
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Yes. We’ve followed one other Data Scientist I job there since May 2026. It came down after 2 days.
New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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 IWho is Mastercard?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.
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Pune, Mahārāshtra, India
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