MastercardLead Data Scientist
Mastercard is hiring a Lead Data Scientist in Gurugram, India. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Builds AI ●●●● is how Level rates this data scientist role: the job is building AI systems.
- The posting mentions LangChain, MLflow and Databricks among its tools and skills.
- Level classes it as a lead role.
- Mastercard has 1,000+ other open roles on Level.
- Posted in the last few days.
AI in this role
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
Lead Data ScientistOur PurposeMastercard 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.
Overview
The Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s fraud and risk product, the team builds and enhances models that analyze billions of transactions and enable real-time detection of fraudulent activity.
We are looking for a highly motivated Lead Data Scientist to drive the next generation of AI-powered fraud detection systems. This is a senior, hands-on technical role that combines deep individual contribution with technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning systems, and large-scale data engineering while contributing directly to Mastercard's mission of enabling secure and trusted commerce worldwide.
Role
As a Lead Data Scientist, you will be expected to be highly hands-on while providing technical leadership in designing, developing, coding, testing, and deploying enterprise-scale AI solutions that enhance fraud detection and risk intelligence capabilities across Mastercard's Security Solutions portfolio. This is not just a model development role; it is a strong fit for someone who enjoys working closely with complex data, exploring patterns, understanding the business and technical problem deeply, experimenting through R&D, and bringing interdisciplinary research ideas—such as advances in NLP, deep learning architectures, graph learning, or agentic systems —into the fraud and security space. The role is also about identifying practical solutions and implementing them in scalable production systems.
You will lead complex AI engineering initiatives while directly contributing to model development, experimentation, system design, and production implementation.
Key responsibilities include:
Lead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
Define, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement.
Hands-on design and implementation of Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.
Work with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.
Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.
Drive the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development, improve quality, and establish best practices across teams.
Set technical direction by evaluating emerging AI technologies, defining adoption patterns, and identifying opportunities to apply them to fraud prevention and security challenges.
Contribute to Mastercard's innovation efforts by publishing top-tier research papers, filing patents, and translating applied AI research into scalable fraud and security solutions.
Ensure solutions meet Mastercard standards for scalability, reliability, explainability, security, responsible AI, and long-term maintainability.
All About You
Demonstrated experience leading AI Engineering, Machine Learning Engineering, Data Science, or applied research initiatives from concept through production deployment.
Expert-level hands-on programming skills in Python and strong experience building production-quality software, reusable frameworks, and scalable AI systems.
Deep understanding of machine learning fundamentals and practical application of supervised learning, ensemble methods, feature engineering, model evaluation, experimentation, and production model risk controls.
Proven track record as a hands-on practitioner developing, deploying, debugging, and improving machine learning solutions in production environments with measurable business impact.
Strong hands-on experience writing, reviewing, testing, and maintaining production-grade code for AI/ML applications.
Experience working with LLMs, prompt engineering, reasoning and planning frameworks, RAG architectures, vector databases, AI agents, and Generative AI application development.
Experience with distributed computing platforms such as Spark, Databricks, or similar big data technologies.
Familiarity with cloud platforms such as Azure, AWS, or GCP and associated AI/ML services.
Strong understanding of MLOps principles, including model deployment, model monitoring, CI/CD, ML lifecycle management, governance, observability, and operational reliability.
Experience working with SQL and large-scale structured and unstructured datasets.
Strong analytical thinking and problem-solving abilities with a passion for solving complex real-world challenges.
Excellent communication, technical leadership, and collaboration skills with the ability to influence architecture decisions and work effectively across cross-functional teams.
Ability to mentor engineers, raise engineering standards, and bring strong learning agility, intellectual curiosity, and thought leadership in AI and machine learning.
Preferred Qualifications
Experience developing fraud detection, risk management, trust & safety, cybersecurity, or financial crime solutions.
Hands-on experience with Hadoop, Spark, Databricks, MLflow, Agentic AI frameworks, reasoning and planning frameworks, LangChain, or similar AI engineering technologies.
Experience building Agentic AI solutions and multi-agent systems.
Exposure to graph learning, anomaly detection, network intelligence, or real-time decisioning systems.
Publications, patents, open-source contributions, Kaggle achievements, or demonstrated research experience in AI/ML.
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.
How we rate this
Lead Data Scientist at Mastercard rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $166k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, ML Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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