Job Title: Manager, Data Scientist Overview: About Mastercard: Mastercard is a leading global technology company in the payments industry. Our mission is to connect and empower an inclusive, digital economy that benefits everyone, everywhere, by making transactions safe, simple, smart, and accessible. Leveraging secure data, robust networks, strategic partnerships, and our commitment to innovation, we enable individuals, financial institutions, governments, and businesses to realize their full potential. With a presence in over 210 countries and territories, we are dedicated to building a sustainable world that unlocks priceless possibilities for all, driven by our core value of decency. About the Team: The Optimization Solutions team focuses on enhancing digital payment performance across all Mastercard products and regions. As consumer preferences evolve towards digital payments, we prioritize delivering a seamless and secure experience. The team analyses digital performance metrics, identifies factors influencing outcomes, and monitors broader industry trends. We generate actionable, data-driven insights and recommendations, collaborate with external stakeholders to implement optimization solutions, and work cross-functionally to ensure alignment and execution. Position: Manager – Data Scientist The Role As part of the Optimization Solutions team, the role involves: • Partner with global, cross functional teams to design, develop, and maintain advanced analytics and data science solutions that drive business impact. • Build, deploy, and scale machine learning models that deliver actionable insights for product optimization, growth, and sales enablement. • Lead the end to end design and technical oversight of complex data science and ML solutions, ensuring scalability, performance, and engineering excellence. • Drive the development, validation, monitoring, and continuous improvement of predictive models, statistical techniques, and algorithms aligned with business objectives. • Establish and uphold best practices across statistical analysis, feature engineering, model evaluation, and performance monitoring. • Translate advanced analytics, model results, and experimental findings into clear, data driven insights for senior business and product stakeholders. • Design and implement robust data pipelines, including ingestion, aggregation, and processing of large-scale, high dimensional datasets. • Research and evaluate emerging AI, machine learning, and data science technologies to identify innovative opportunities and potential risks. • Contribute to the development and adoption of frameworks, standards, and guardrails that ensure responsible, compliant, and resilient AI solutions. • Apply appropriate metrics, benchmarking, and measurement frameworks to assess solution effectiveness and business value. All About You Education & Experience: • Strong academic background in Computer Science, Data Science, Technology, Mathematics, Statistics, or a related field—or equivalent work experience. • Experience in data management, mining, analytics, reporting, product development, and quantitative analysis. Technical Skills: • Proficiency in Python, Spark, Hadoop platforms (Hive, Impala, Airflow, NiFi), and SQL. • Familiarity with machine learning frameworks, libraries, data structures, and software architecture. • Experience with BI platforms like Tableau or Power BI is a plus. Analytical & Communication Skills: • Strong analytical skills with a proven ability to translate data into compelling insights. • Experience interacting with stakeholders to understand technical needs and deliver effective solutions. • Ability to articulate the business value of products and contribute to sales enablement. Work Style: • Comfortable working in a fast-paced, deadline-driven environment. • Effective as both a team player and an individual contributor. To find salary ranges and other disclosures for US and Europe countries where applicable, visit https://hrportal.ehr.com/mastercard/Home/Compensation/Compensation/more#. In the US, see link for “salary structures”. For more information on benefits, visit People Place and review the tabs for Benefits and for Time Off & Leave.