Capacity Operations and Analytics Manager

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

This role manages and optimizes GPU capacity and other compute resources across cloud providers, focusing on data modeling, reporting, automation, and performance analysis to drive infrastructure efficiency and resource planning. It involves leveraging AI tools and statistical modeling to improve operational efficiency and inform strategic decisions.

What you'd actually do

  1. Manage and optimize GPU capacity and other compute resources across various cloud service providers to meet growing demands and ensure efficient utilization.
  2. 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.
  3. Analyze the technical and business needs for GPU capacity and other compute resources from various internal and external teams.
  4. Identify performance bottlenecks in day-to-day usage of compute resources and collaborate with relevant infrastructure teams to resolve them.
  5. Drive infrastructure resource efficiency initiatives in partnership with engineering, finance, and product teams.

Skills

Required

  • 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
  • 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 employing 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
  • Ability to operate effectively amidst uncertainty and rapidly changing business conditions, with an agile mindset and a commitment to ongoing improvement

Nice to have

  • A proven track record of large-scale computing operations and planning is a plus.

What the JD emphasized

  • Experience with Cloud Service Providers such as AWS, Azure, GCP, and OCI is required.
  • Demonstrated experience in employing 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

Other signals

  • manage and optimize GPU capacity
  • build, develop, and maintain data models, reporting systems, data automation systems, dashboards, and performance metrics
  • analyze the technical and business needs for GPU capacity
  • identify performance bottlenecks
  • drive infrastructure resource efficiency initiatives
  • develop and enhance tooling for our cloud infrastructure and analytics platform to optimize resource usage and performance
  • partner and cross-collaborate with Finance, Product, Service Owners, and Infrastructure Engineering teams
  • lead multi-year budget-based compute resource planning