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Mastercard

Processes payments across a global network

Lead Data Scientist - Multiple Teams

Applied Machine LearningConfirmed live in the last 24 hours
Full-timePune, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • 8-12 years of overall career experience in a data science function supporting products.
  • Design, develop, and productionize machine learning and data science solutions for complex business problems.
  • Develop traditional machine learning solutions, including 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 generative AI frameworks, particularly LangChain and LangGraph.
  • Optimize data pipelines and machine learning and generative AI workflows for performance, scalability, maintainability, and reliability.
  • Evaluate generative AI solutions for answer quality, groundedness, reliability, latency, and cost.
  • Develop robust, reusable Python components following sound software-engineering practices.
  • Use PySpark to process, transform, and analyze large datasets 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 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.

Responsibilities

  • Drive the evolution of B&MI products, focusing on data science and engineering impact.
  • Explore, apply, and develop data science for product development.
  • Identify and evaluate AI/ML techniques and tools to deliver models and analytics for product development.
  • Innovate and determine data science approaches and technologies to solve business problems and generate business insights and recommendations.
  • Partner with product, engineering, sales, and other organizational roles to prioritize data science problems within a customer-centric framework.
  • Evaluate analytics-solution trade-offs, considering performance, usability, technical feasibility, timelines, and stakeholder perspectives.
  • Break large solutions into smaller, releasable milestones to gather data and feedback from product managers, clients, and other stakeholders.
  • Promote releases to users, incorporate feedback, and track usage to inform future development.
  • Work with small cross-functional teams to define vision and establish team culture and processes.
  • Focus on key drivers of organizational value and prioritize operational activities accordingly.
  • Identify and act on product improvement opportunities.
  • Provide technical leadership and technical and non-technical mentoring to junior colleagues as a senior member of the data science team.

About the Company

Mastercard

Processes payments across a global network

Mastercard operates a global payment network connecting financial institutions, merchants, businesses and consumers. It authorizes, clears and settles transactions between participating institutions, while banks issue Mastercard-branded cards and extend credit to their customers. Its services also include fraud detection, identity verification, payment tokenization, data analysis and consulting for organizations managing commerce and financial operations. The company earns network fees linked to payment volumes and processed transactions, along with fees for its services and solutions. Founded by a group of banks in 1966, Mastercard is headquartered in Purchase, within Harrison, New York.

Company size
10,001+
Company stage
IPO
Headquarters
Town of Harrison, New York
Founded
1966

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