Lead Data Scientist​ - R&D

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​ - R&D Overview We are looking for a talented Lead Data Scientist to join with our Foundry Research and Development team to build innovative products delivered at scale to global markets. The Foundry Research and Development team is built on a foundation of research and development, mining innovation internally, innovating new product lines with emerging technology, managing new products from inception to market validation and engaging strategically with start-ups to shape the future of commerce with and for our customers. This team operates across geographies and technology domains, tackling complex challenges to bring innovative payment solutions and services to market. At Mastercard Foundry, we empower innovation by exploring emerging technologies and building cutting-edge solutions that help define the future of commerce globally. Responsibilities: • Leads the technical design, development, and review of complex, scalable machine learning models, predictive algorithms, and statistical solutions to address high-priority business challenges, ensuring alignment with Mastercard’s organizational standards and best practices. • Guides the refinement and optimization of models and algorithms through feature engineering, hyperparameter tuning, and validation techniques to ensure performance, reliability, and robustness in production environments. • Collaborates with cross-functional teams, including AI/ML engineering, product, and business stakeholders, to support the deployment, scaling, and operationalization of models while maintaining technical excellence. • Translates complex data science insights into clear, actionable recommendations to support decision-making and innovation initiatives. • Contributes to the development and adoption of best practices, standards, and knowledge sharing in statistical analysis, feature engineering, model tuning, and validation. • Maintains comprehensive technical documentation of models, processes, and methodologies to support reproducibility, knowledge transfer, and continuous improvement. • Mentors and guides junior team members through hands-on support, reviews, and on-the-job learning to strengthen data science capabilities across the team. All About You: • Expertise in designing and delivering production grade machine learning, deep learning, and NLP solutions for both supervised and unsupervised use cases, including solution architectures and end to end pipelines. • Strong proficiency in Python and the data science ecosystem (e.g., numpy, pandas, sklearn, keras, torch, transformers, langgraph), with experience building and integrating APIs using frameworks such as FastAPI and working with JSON based services. • Solid understanding of statistical techniques and optimization methods, including hypothesis testing, feature engineering, model tuning, validation, and performance evaluation. • Experience working with large scale data processing and analytics technologies, including PySpark, SQL (Cosmos DB, Postgres), and parallel data processing concepts. • Familiarity with modern AI/ML practices such as Agentic AI patterns, prompt engineering, MLOps (e.g., MLflow), and exposure to LLM fine tuning and evaluation approaches. • Experience working in cloud-based environments, leveraging cloud native services (e.g., Azure), with working knowledge of Databricks, Unix based systems, and code configuration management tools such as GitHub or Bitbucket. • Passion ... (truncated, view full listing at source)