Lead Data Scientist (AI/ML, Python, Langchain, GenAI, Pyspark, SQL)

Mastercard is hiring a Lead Data Scientist (AI/ML, Python, Langchain, GenAI, Pyspark, SQL) in Pune.

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 Scientist (AI/ML, Python, Langchain, GenAI, Pyspark, SQL)

Lead, Data Scientist

• Our Purpose

The Business & Market Insights (B&MI) team within Mastercard's Services division creates products that help customers including financial institutions and merchants understand their customers and the market better. The B&MI Data Science team uses data from Mastercard transactions and other sources to build models and solutions that are needed for these products, at a globally scalable level across the different customer lifecycle stages.

This role is for a Data Scientist that will work across Product, Client Services & Delivery, Data & Engineering and the broader teams, to deliver truly differentiated products driven by analytics & AI, leveraging rich data resources, AI platform capabilities and near real-time execution.

We are developing multiple new products and capabilities using analytics & AI, and this role is central in that vision, from concept, technical design to market release and optimization. This role is positioned to drive new ideas forward by combining creative thought leadership with innovation and industry best practices. By entering in the early phase of a truly transformative and impactful strategic initiative, the experience is certain to be rewarding.

This role requires the successful candidate to have the following:

• 8-12 years of overall career experience in Data Science function supporting Products
• Design, develop, and productionize machine learning and data science solutions that solve complex business problems within BMI.
• Develop traditional ML solutions covering feature engineering, model training, validation, evaluation, and deployment.
• Perform exploratory data analysis and translate complex datasets into actionable insights.
• Build Generative AI applications using Python and modern GenAI frameworks, particularly LangChain and LangGraph.
• Optimize data pipelines and ML/GenAI workflows for performance, scalability, maintainability, and reliability.
• Evaluate GenAI solutions for answer quality, groundedness, reliability, latency, and cost, rather than relying solely on prototype-level demonstrations.
• Develop robust and reusable Python components following sound software-engineering practices.
• Use PySpark to process, transform, and analyze large datasets efficiently in distributed environments.
• Write advanced, optimized SQL for data extraction, transformation, feature development, and analytical workloads.
• Collaborate with product managers, AI engineers, software engineers, and other data scientists to translate business requirements into technical solutions.
• Apply appropriate controls for data privacy, security, responsible AI, monitoring, and explainability.
• Conduct code reviews, establish reusable technical patterns, and mentor less-experienced data scientists.

The successful candidate’s primary responsibilities will be to:

• Drive the evolution of B&MI products with an impact focused on data science and engineering
• Participate in the exploration, application and development of data science for product development
• Participate in identification and evaluation of AI/ML techniques and tools to deliver models and analytics for product development
• Continuously innovate and determine new data science approaches & technologies to solve business problems and generate business insights & recommendations
• Partner with roles across the organization including product, engineering, and sales to determine the highest priority data science problems to solve in a customer-centric framework
• Evaluate trade-offs between many possible analytics solutions to a problem, taking into account performance, usability, technical feasibility, timelines, and differing stakeholder opinions to make a decision

About Mastercard

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