Mastercard
Pune, Maharashtra, India Posted 5d ago
The Global Business Solutions Center (GBSC), Strategy, Data & Insights team acts as an internal consultant supporting the Shared Services Organization at Mastercard.
Key Responsibilities
Lead and develop the Data Science team by setting clear priorities and performance expectations aligned to strategy, providing technical and people leadership, and promoting best practices across model development, validation, documentation, and deployment.
Drive efficiency and continuous improvement by tracking initiative success, refining processes, and implementing new methods and analytical approaches.
Partner with business leaders to translate strategy into data science use cases, delivering clearly communicated, actionable insights and serving as a trusted advisor on data driven decisions.
Oversee the design and delivery of predictive, statistical, and machine learning models, ensuring analytical approaches are explainable, scalable, and grounded in rigorous experimentation, hypothesis testing, and causal analysis to address key business questions.
Partner with data engineering, platform, and analytics teams to ensure high quality, usable data, enable production ready analytics and models, and influence data science tooling, architecture, and methodologies.
Establish and enforce standards for model governance and lifecycle management, ensuring responsible, explainable analytics and compliance with data privacy, security, and regulatory requirements.
Stay current on emerging data science techniques and tools, applying new technologies and frameworks to improve team productivity and enhance the efficiency and impact of analytics solutions.
Measure and communicate the business impact of data science initiatives.
Requirements
Undergraduate degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field.
Proven track record of delivering analytics or machine learning solutions that drive business outcomes.
Prior experience as a People Leader of Data Science teams.
Advanced knowledge of data science programming languages such as R and Python.
Experience working with large datasets and modern data platforms.
Experience with model deployment, monitoring, and lifecycle management.
