Senior Data Scientist (AI, ML, Pyspark, SQL)

Location: Pune, India
Department: Data Science

Mastercard's Business & Market Insights (B&MI) team creates products that help customers understand their customers and the market better.

The Senior Data Scientist (AI, ML, Pyspark, SQL) will work across Product, Client Services & Delivery, Data & Engineering and the broader teams to deliver truly differentiated products driven by analytics & AI.

Responsibilities:

• 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.
• Optimize data pipelines and ML workflows for performance, scalability, maintainability, and reliability.
• 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.
• Apply appropriate controls for data privacy, security, responsible AI, monitoring, and explainability.

Good to have:

• Understanding of building Generative AI applications using Python and modern GenAI frameworks.

The successful candidate's primary responsibilities will be to:

• Drive the evolution of Business & Market Insights products by applying Data Science and Artificial Intelligence to create models, tools and capabilities.
• 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.
• Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients and other stakeholders.
• Evangelize releases to users, incorporating feedback, and tracking usage to inform future development.
• Work with small, cross-functional teams to define vision, establish team culture and processes.
• Consistently focus on key drivers of organization value and prioritize operational activities accordingly.
• Identify and act upon product improvement opportunities.
• Provide technical leadership of junior colleagues as a senior member of the data science team, through technical and non-technical mentoring.