• Develop and productionize machine learning models for a range of healthcare products, including monitoring controlled substance handling and patient privacy, and inventory management • Define and track KPIs to measure model performance and business impact • Collaborate with product teams and customers to scope analytics projects and build prototypes. Translate ambiguous business problems into well defined, quantitative, and testable hypotheses. • Communicate technical findings to both technical and non-technical stakeholders, including customers. Present results and recommendations in customer-facing meetings. • Inform product roadmaps, evaluate technical feasibility, and assess project risks. • Quickly ramp up on new healthcare data sources as products evolve and adapt to new ML techniques and tools as needed for product requirements • Raise the skill level of the entire analytics team by modeling strong research communication through team presentations and documentation. Drive collaboration through code reviews, experimental design feedback, and participation in model evaluation discussions.

• Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI). • Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions. • Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys), optimizing for value-added, latency, and cost. • Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring that catches quality drift early. • Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows. • Design and support agentic workflows where they add real product value, with clear constraints, observability, and fallbacks. • Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning where relevant, validation, deployment, monitoring, incident response, and continuous improvement. • Raise org-wide leverage by creating reusable assets (evaluation harnesses, shared datasets, templates, documentation) and running enablement workshops. • Communicate insights and tradeoffs clearly to technical and non-technical stakeholders, turning analyses into decisions and measurable impact. • Champion responsible, privacy-aware AI: appropriate data handling, bias/fairness considerations where applicable, and human-in-the-loop workflows when needed.

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 Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview Are you an experienced data scientist with a passion for technological innovation, have a can-do attitude and strong sense of ownership? The Small and Medium Enterprise (SME) platform is the perfect opportunity for you to assist in the design & development of a world-class unified platform, enabling Small and Medium Enterprises globally. The SME team is looking for a senior data scientist to own the development of machine learning models and business intelligence projects. You will also work closely with cross-functional teams, including data engineers, data analysts, and software engineers, to drive data-driven decision making throughout the organization. Key Responsibilities • Analyze various data sources to deliver data-driven and actionable insights for small and medium-sized enterprises in Mastercard network.• Develops sophisticated multivariate models and analytic solutions (e.g., dashboards, machine learning models), utilizing complex statistical and data science techniques and methods.• Self-stater that can work independently with ambiguous requirements and proactively identify insights opportunities from the data.• Communicate with cross functional teams and business partners to help drive their ability to leverage insights from data. All about you • Demonstrated ability to combine, interrogate, and manipulate large data sets using big data tools (Hadoop, spark etc)• Extensive experience of using common programming languages (Python preferred) and different statistics and machine learning libraries (Pandas, scikit-learn, PySpark etc)• Understand the trade-off between different machine learning model validation metrics (AUC, recall etc.) to achieve the most optimal mode performance based on the business needs.• Knowledge of version control systems (Git etc).• Ability to directly communicate with business partners to translate statical findings into business insights.• Experience with BI visualization tools (Tableau, Domo, Power BI etc) is highly preferred.• Experience of optimizing big data ETL pipeline is preferred. • Good to have experience of software engineering. What makes you stand out • Experience in deploying machine learning models to cloud-based production environments.• Experience with Databricks. • Experience with the innovative application of Generative AI in commercial settings.• Background in the financial industry. • Solid foundation in software engineering, with a focus on maintaining high code quality, effective version control, and rigorous testing practices, ensuring the development of robust and reliable software solutions. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: - Abide by Mastercard's security policies and practices; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

• Partner with cross-functional teams to define high-impact product questions, success metrics, and decision frameworks. • Analyze user behavior, creator activity, content ecosystems, retention, growth, monetization, and social dynamics to identify opportunities and risks. • Design and analyze experiments, including A/B tests and other causal measurement approaches, to evaluate product launches, campaigns, and strategic initiatives. • Define and improve core product metrics, dashboards, diagnostic views, and recurring reporting that help teams understand what is changing and why. • Build durable analytical assets such as reusable notebooks, data marts, metric definitions, self-serve dashboards, and analysis templates. • Use AI-assisted and agentic workflows to accelerate analysis, automate repetitive investigation, improve documentation, and build lightweight internal tools for common analytical questions. • Collaborate with Data Engineering and product teams to improve data quality, instrumentation, modeling, and source-of-truth definitions. • Communicate findings clearly to technical and non-technical audiences, including senior stakeholders, and influence product decisions through evidence and judgment.