Senior Data Scientist

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 Senior Data Scientist Job Title: Senior Data Engineer, Value Quantification – Product Data & Analytics (CNPF) Product Data & Analytics is a centralized global team that enables Mastercard product business units to make better data‑driven decisions. We build internal analytics partnerships that strengthen focus on business performance, portfolio and revenue optimization, initiative tracking, new product development, and go‑to‑market strategies. These capabilities are underpinned by our data platforms that enable insights to be delivered in a standardized, scalable, and cost‑efficient manner. Key Responsibilities Strategic Support • Design and implement value enablement frameworks that optimize pricing strategies, enhance pre-sales propositions, and ensure customer success. • Collaborate with global and regional teams to tailor solutions that meet regional business needs and align with Mastercard's objectives. • Provide data-driven insights and recommendations to optimize pricing, pre-sales strategies, and customer success outcomes. • Develop frameworks, project structures, and presentations to communicate key strategic initiatives. • Conduct data integrity checks and ensure quality and reliability in data used for analysis. • Translate complex business problems into analytical solutions that support strategic decision-making. Technical Leadership • Lead the development of advanced analytics tools, such as ROI calculators and value dashboards, to measure and optimize business performance. • Utilize Python, SQL, and BI tools (e.g., Tableau, Power BI) for data analysis, modeling, and visualization. • Create dynamic, interactive dashboards to provide stakeholders with actionable insights. • Integrate AI/ML models to enhance the accuracy and efficiency of analytical tools (good-to-have, not mandatory). • Drive process efficiency and scalability through automation and advanced analytics techniques. Value Enablement Initiatives • Develop frameworks for measuring and tracking customer value realization. • Design predictive models and real-time insights to create tailored customer solutions and business cases. • Build self-service analytics tools to empower stakeholders with actionable insights. • Encourage a culture of innovation by promoting participation in Sandbox challenges and regular brainstorming sessions. • Develop flexible and agile solutions based on a deep understanding of business goals and market context.