Director, Data Science Online

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. Director, Data Science Overview The Security Solutions Data Science team is responsible for delivering Artificial Intelligence (AI) and Machine Learning (ML) models that support Mastercard’s Identity and risk products across the payment networks. These models are designed to be production-ready and to power high-value capabilities that protect digital transactions and enable trusted decisioning at scale. Beyond model development, the organization is responsible for building scalable, repeatable, and resilient data science capabilities that cover the end-to-end lifecycle of machine learning solutions, from data acquisition and feature engineering through experimentation, validation, deployment, and monitoring. These systems must not only perform effectively in production, but also be built in a way that is industrialized, maintainable, and aligned with broader business and platform needs. Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. Within this mission, the Identity Data Science portfolio plays a significant role in developing intelligence-driven solutions that improve how risk is understood and managed across the merchant lifecycle. We are looking for a highly skilled and strategic Director to lead a Data Science team that focuses on solving merchant risk during onboarding and monitoring. This role is critical in driving the research and development of a merchant registry and profiling capability within the Identity Data Science portfolio. This includes setting the strategy for how merchant intelligence is built and scaled, guiding the development of reusable data science assets and profiling frameworks, and ensuring strong execution across product, engineering, and data science partners. You will help shape both the technical direction and operational model needed to turn this capability into a durable and scalable advantage. Define and execute the strategy for solving merchant risk during onboarding and monitoring through the research and development of a merchant registry and profiling capability Lead, coach, and develop high-performing teams of Data Scientists, including hiring, mentorship, and performance management Guide the design of scalable machine learning and analytical systems using modern data and cloud platforms such as Databricks and Sparks. Establish best practices for problem framing, technique selection, experimentation, and validation across the team Ensure teams identify appropriate approaches for business problems and rigorously validate solutions against both technical and business outcomes Partner closely with Product, Engineering, and other stakeholders to translate strategic needs into data science roadmaps and deliverables Drive Agile delivery practices that support iterative development, measurable outcomes, and continuous improvement Promote reusable data assets, standardization, and scalable workflows that strengthen long-term execution Communicate strategy, progress, trade-offs, and business value clearly to senior leadership Balance long-term vision with near-term delivery to maximize impact and time-to-value Advanced degree (Master’s or PhD preferred) in Data Science, ML, or related field Extensive experience leading a Data Science team and drive innovation with inspiration. A proven track record of deploying high performance machine learning models at scale in a production environment Strong proficiency with Python, SQL, along with experience using scalable Machine Learning and Cloud frameworks Strong ability to guide teams in identifying appropriate techniques and validating solutions rigorously Critical thinking and a drive to produce high-quality work, ensuring that all solutions meet rigorous standards Demonstrated success translating complex business problems into strategic data science initiatives. Strong understanding of Agile methodologies, with the ability to drive iterative delivery across cross-functional teams Excellent stakeholder management, communication to both technical and non-technical audiences, and leadership skills Experience building or scaling shared data science platforms, registries, or enterprise data assets Familiarity with merchant risk, fraud, or identity ecosystems Exposure to governance frameworks for model validation and AI systems In the US or Canada, if y