Lead Data Scientist (R&D)
Company: Mastercard
Location: Toronto, Canada, Canada
Type: Full-time
Posted: 2026-05-13
About this role
- 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
- 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...