NVIDIA’s Hardware Infrastructure organization is seeking a Senior Data Scientist to support EDA datacenter observability, hardware reliability, and capacity forecasting. In this role, you will work closely with hardware, software, and infrastructure engineering teams to analyze large-scale observability and telemetry data generated by EDA workloads running across global CPU and GPU compute clusters.
We work with observability and platform teams to turn raw infrastructure and workload data into meaningful conclusions. These conclusions will help improve reliability, availability, performance, and long-term datacenter scaling. Our work will directly inform operational decisions and long-term planning for NVIDIA’s rapidly growing EDA environment.
What You’ll Be Doing:
Collaborate with hardware, software, infrastructure, and observability teams to define analytical requirements for EDA datacenter monitoring and reliability
Examine large-scale observability data, hardware health signals, and workload telemetry to identify reliability risks, performance bottlenecks, and inefficiencies
Create performance indicators, dashboards, and analytical frameworks that measure hardware reliability, workload stability, availability, and utilization
Build statistical and machine learning models for anomaly detection, failure pattern analysis, and reliability improvement
Develop forecasting models to predict datacenter growth, capacity needs, and scaling requirements across compute, storage, and networking
Partner with observability engineers to influence data collection, enrichment, and retention strategies that support high-quality analysis
Translate sophisticated analyses into clear, actionable insights for both technical and non-technical collaborators
Continuously improve data quality, analytical workflows, and methodologies to support reliable, scalable EDA infrastructure
What We Need to See:
MS (preferred) or BS in Computer Science (or equivalent experience) or a related field with at least 8+ years of experience applying data science, statistics, or machine learning to large-scale, distributed systems or infrastructure data
Proficiency in Python and SQL, with experience working with large time-series, or operational datasets
Experience performing exploratory data analysis, feature engineering, and model validation
Familiarity working with observability or telemetry data and turning raw signals into actionable insights
Ability to take ownership of analytical projects and drive them from problem definition through delivery, in collaboration with multi-functional partners
Experience communicating results through dashboards, reports, and data-driven recommendations
Collaboration, planning, and interpersonal skills
Flexibility and adaptability in a dynamic environment with evolving requirements
Ways to Stand Out from the Crowd:
Experience with datacenter infrastructure, hardware reliability analysis, or workload performance modeling
Familiarity with EDA workflows, HPC environments, or large-scale compute platforms
Experience enabling forecasting, managing farm resources, or long-range infrastructure analytics
Exposure to observability platforms or data systems such as Spark, Elastic/OpenSearch, Grafana, Prometheus, or similar technologies, and experience working closely with infrastructure or observability engineering teams
A track record of driving process improvements using data and sharing knowledge across teams
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Top Skills
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
