• Manage and optimize GPU capacity and other compute resources across various cloud service providers to meet growing demands and ensure efficient utilization. • Build, develop, and maintain data models, reporting systems, data automation systems, dashboards, and performance metrics that support NVIDIA Infrastructure governance programs and strategic capacity decisions. • Analyze the technical and business needs for GPU capacity and other compute resources from various internal and external teams. • Identify performance bottlenecks in day-to-day usage of compute resources and collaborate with relevant infrastructure teams to resolve them. • Drive infrastructure resource efficiency initiatives in partnership with engineering, finance, and product teams. • Develop and enhance tooling for our cloud infrastructure and analytics platform to optimize resource usage and performance for NVIDIA and its customers. • This includes crafting and developing tools for automating workflows and potentially leveraging AI techniques to extract useful signals and insights from generated data. • Partner and cross-collaborate with Finance, Product, Service Owners, and Infrastructure Engineering teams to align cloud capacity management with company goals and develop Infrastructure and Service Level Key Performance Indicators (KPIs) to match Customer satisfaction. • Lead multi-year budget-based compute resource planning with engineering.

• Support supply chain operations by collecting, validating, and maintaining critical datasets required for upcoming initiatives and launches • Build, maintain, and improve Bills of Materials (BOMs) to ensure supply chain data accuracy • Develop and maintain Power BI dashboards to provide operational and supply chain insights • Analyze inconsistent or incomplete datasets and implement improvements to data quality and reliability • Coordinate changes to databases, including testing and implementation of updates • Design, implement, and maintain database structures and ETL processes to support large‑scale data needs • Write logical and physical database descriptions and define identifiers for database management systems • Modify and optimize existing databases and reporting processes as business needs evolve • Partner with technical, business, and operations stakeholders to define requirements, metrics, and reporting standards • Test data systems and reports, troubleshoot issues, and implement corrections as needed

• Support supply chain operations by collecting, validating, and maintaining critical datasets required for upcoming initiatives and launches • Build, maintain, and improve Bills of Materials (BOMs) to ensure supply chain data accuracy • Develop and maintain Power BI dashboards to provide operational and supply chain insights • Analyze inconsistent or incomplete datasets and implement improvements to data quality and reliability • Coordinate changes to databases, including testing and implementation of updates • Design, implement, and maintain database structures and ETL processes to support large‑scale data needs • Write logical and physical database descriptions and define identifiers for database management systems • Modify and optimize existing databases and reporting processes as business needs evolve • Partner with technical, business, and operations stakeholders to define requirements, metrics, and reporting standards • Test data systems and reports, troubleshoot issues, and implement corrections as needed

• Ownership of Data Analysis & Visualization: Monitor and improve critical KPIs such as fraud costs, chargeback volume, fraud rates and revenue leakage • Strategic Communication: Regularly communicate outcomes and insights to cross-functional stakeholders, including senior leadership, to guide strategic decision-making for process and performance improvement • Development of Single Source of Truth (SSOT) Dashboards and Reporting Models: Work alongside DS & Broader Fraud & Identity analysts to implement robust models in DBT and Snowflake, and design dashboards that provide a unified view of business-critical Shopper fulfillment, Shopper Payment and PoS (point of sale) Checkout data • Collaboration on Roadmaps: Work with Product, Data Science, and Xfn Analytics teams to understand growth and risk factors related to new and future fraud vectors • Operational Data Analysis: Analyze shopper behavioral signals to identify anomalous patterns indicative of fraud or policy abuse • A/B Testing & Experimentation: Design and conduct A/B and multivariate tests by developing test plans, defining success metrics, analyzing, and transforming data into actionable insights • Xfn Collaboration: Collaborate with the Engineering and Product team to instrument new product features and ensure great event tracking and data integrity • Automation and Reporting: Build automated reporting systems to keep operations leaders informed of trends, variations, and opportunities across geographies and teams