Data Scientist I
Mastercard — Pune, Maharashtra
₹11–18 LPA (HireSetu estimate) · Fresher-friendly · Full-time
HireSetu listing reference dc4627 · employer requisition R-278159 — confirm requirements below, then apply on the employer site.
Quick answer: A GenAI engineering job wearing a Data Scientist I title, sitting inside Mastercard's Global Risk and Compliance team. The work is transaction monitoring for anti-money laundering, so the RAG pipelines and fine-tuned models you build have to hold up to audit and regulatory review.
From Mastercard's job posting
Data Scientist I
Who is Mastercard?
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
- 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 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
The employer’s company overview, benefits and equal-opportunity statement are left out here; they are on the original posting.
Text from Mastercard's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
HireSetu's note
The grade says one, the stack says three
The requirements ask for expertise in RAG pipelines, MCP-based integrations, tool calling and LangChain or LangGraph, plus pipelines for fine-tuning large language models. That is a shipped-projects list. Nobody is going to hand you these on week one, so read the I as a pay band and a reporting line, not as a promise of training.
The setting matters just as much. This sits in Global Risk and Compliance, building AI for AML transaction monitoring, which is work that gets examined. Explainable beats clever here.
Lead your CV with deployed GenAI work
Put the GenAI you have actually run in the first four lines: a RAG system you built and how you measured whether its answers were right, a fine-tuning job, an evaluation harness. Then Python with PyTorch or TensorFlow, one cloud from AWS or Azure, and Databricks if you have used it.
Computer vision gets one bullet in the listing among ten. Keep it to a single line unless image work is genuinely your strength. Recruiters on this team will scan for LLM application experience first.
Study how you would catch a bad answer
The evaluation and observability requirements are where most applicants are thin: hallucination detection, factuality scoring, bias checks, quality scoring, then latency and cost tracking in production. Be able to sketch how you would build one of these and what you would do with the numbers.
Round that out with MLOps basics, model versioning, CI/CD for ML and drift monitoring, and with a clear view on when fine-tuning earns its cost over a better prompt. This is general background for the field, not a preview of anyone's interview.
The domain and the commute
AML transaction monitoring runs on alerts, and the vast majority of those alerts turn out to be nothing. The expensive part of the problem is the false positives. An hour reading about why that volume hurts banks gives you a real question to ask the team about what their current alert loads look like.
The job is in Pune and the page says nothing about days in office, so settle the weekly split with the recruiter before you go deep in the process.
Drafted with AI from Mastercard's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.
How to apply
- Open Mastercard's careers site with the Apply button.
- Create a Workday account for Mastercard. Workday logins are separate for every company, so an account made for another employer won't work here.
- Upload your resume and check every auto-filled field; Workday often misreads dates and job titles.
- Answer the screening questions, submit, and look for the confirmation email from Workday.
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