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.

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
10+ years of overall experience in cloud computing, specifically in managing or sourcing GPU capacity with cloud service providers.
A proven track record of large-scale computing operations and planning is a plus.
Strong technical proficiency in cloud architecture, development and deployment, and managing large data sets.
Deep understanding of cloud service models (IaaS, PaaS, SaaS) and cloud infrastructure technologies.
Experience with Cloud Service Providers such as AWS, Azure, GCP, and OCI is required.
Demonstrated experience in leveraging AI tools and techniques to extract useful signals and insights from data, specifically to improve resource usage and automation.
Strong understanding and practical application of statistical modeling and machine learning methodologies for improving operational efficiency and informing strategic capacity decisions.
Proficiency with data analytics, visualization, and monitoring tools such as Kibana, Grafana, Splunk, Prometheus, Tableau, Plotly.
Knowledge of analytics, statistical modeling, and machine learning methodologies.
Excellent communication and interpersonal skills, with the ability to collaborate effectively with various departments and influence strategic decisions.
Ability to operate effectively amidst uncertainty and rapidly changing business conditions, with an agile mindset and a commitment to ongoing improvement.

Capacity Operations and Analytics Manager

Key skills