ZipRecruiter

Log In

1

Nvidia Data Analytics Jobs (NOW HIRING)

Nvidia

OR · On-site

$126K - $166K/yr

NVIDIA CUDA-X brings accelerated computing to data science libraries, query engines, and vector ... Analytical approach and ability to synthesize user research, engineering feasibility, and market ...

Nvidia

Senior Product Manager, STX

Santa Clara, CA

$148K - $196K/yr

NVIDIA data centers are powering the most sophisticated, groundbreaking research and AI products ... Analytical Thinking: able to build objective measurements of schedules, economic trade-offs, and ...

Nvidia

Senior Data Analyst - Finance

Santa Clara, CA · On-site

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... analytics with strong proficiency in Power BI and/or Tableau. * Hands-on experience with SAP data ...

Nvidia

OR · On-site

At NVIDIA, our solutions architects work across different teams and enjoy helping customers with the latest Accelerated Data Analytics and Deep Learning software and hardware platforms. We're looking ...

next page

Showing results 1-20

People also search for

All JobsNvidia Data Analytics Jobs

Nvidia Data Analytics information

See salary details

$24

$54

$94

How much do nvidia data analytics jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for nvidia data analytics in the United States is $54.75, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $62.02 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Nvidia Data Analytics professional, and why are they important?

To excel as an Nvidia Data Analytics professional, you need strong analytical skills, expertise in statistics, and a solid background in computer science or data science, often supported by relevant degrees or certifications. Proficiency with Nvidia’s GPU computing platforms (such as CUDA), data analytics tools (like Python, R, and SQL), and experience with machine learning frameworks are typically required. Excellent problem-solving abilities, communication skills, and a collaborative mindset help you interpret data insights and work effectively within multidisciplinary teams. These abilities are crucial for leveraging advanced analytics to drive innovation and informed decision-making at Nvidia.

What is the difference between Nvidia Data Analytics vs Data Scientist?

AspectNvidia Data AnalyticsData Scientist
Required CredentialsBachelor's in Computer Science, Data Analytics, or related fields; knowledge of Nvidia toolsBachelor's or higher in Computer Science, Statistics, or related fields; often advanced degrees
Work EnvironmentTech companies, data centers, AI labs using Nvidia hardware and softwareVarious industries including tech, finance, healthcare; research and development roles
Employer & Industry UsagePrimarily in AI, machine learning, and data processing with Nvidia platformsAcross industries for data analysis, modeling, and insights generation

While Nvidia Data Analytics focuses on leveraging Nvidia hardware and software for data processing and AI tasks, Data Scientists perform broader data analysis, modeling, and interpretation across various tools and platforms. Both roles require strong analytical skills, but Nvidia Data Analytics is more specialized in Nvidia technologies.

What are some common challenges faced by data analytics professionals at Nvidia, and how are they typically addressed?

Data analytics professionals at Nvidia often encounter challenges such as working with large, complex datasets and ensuring data quality across diverse sources. Additionally, they must stay updated with rapidly evolving analytics tools and technologies used within the company. Collaboration is key, as analytics teams regularly partner with engineering, product, and business units to align insights with strategic goals. To address these challenges, Nvidia fosters a culture of continuous learning, provides access to advanced computing resources, and encourages cross-functional teamwork.

What is an Nvidia Data Analytics professional?

An Nvidia Data Analytics professional is someone who uses Nvidia's hardware and software solutions to analyze large datasets, often leveraging GPU acceleration for faster data processing and advanced analytics. These professionals may work with Nvidia tools like RAPIDS, CUDA, and AI frameworks to perform tasks such as data preprocessing, machine learning, or deep learning. They play a key role in optimizing data workflows, improving model performance, and helping organizations make data-driven decisions, particularly in industries requiring high computational power.
Infographic showing various Nvidia Data Analytics job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $113,873 per year, or $54.7 per hour.
Solutions Architect, Infrastructure

Solutions Architect, Infrastructure

Nvidia

Redmond, WA • On-site

Full-time

Posted yesterday

Job description

Do you thrive on taking a strategic product from launch to gotomarket at scale across the world's largest customers? NVIDIA is looking for an Infrastructure Solutions Architect to lead deployment and bringup of our nextgeneration Data Center GPUs and networking platforms.

As part of the NVIDIA Solutions Architecture team, we navigate uncharted technical and organizational spaces - serving as the bridge between early platform readiness, cloud engineering teams, product strategy, and largescale customer deployments. We are looking for Solution Architects to combine handson infrastructure expertise with multi-functional leadership to accelerate adoption of NVIDIA technologies across worldwide cloud hosting providers and large enterprise environments.

What You'll Be Doing:

  • Lead endtoend execution for Hyperscaler customers to rapidly bring NVIDIA Data Center GPU and networking platforms to market at scale.

  • Drive strategic partnership and alignment with Product teams to understand roadmap intent, codefine critical metrics, and ensure unified direction across technical, sales, and leadership organizations.

  • Influence without authority across Product, Engineering, Sales, Operations, and CSP customers, driving clarity, alignment, and unblock paths for scaleup.

  • Analyze deployment and performance data, identifying product health trends, system bottlenecks, and operational risks.

  • Solve challenging technical problems involving GPUs, networking, drivers, containers, firmware, and distributed system interactions.

  • Deliver streamlined executivelevel communication on status, risks, progress, and required decisions.

  • Collaborate with Product and Engineering, enabling future improvements in platform design, validation, and operational workflows.

What We Need to See:

  • BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or similar, or equivalent experience.

  • 4+ years experience in Solutions Architecture, Infrastructure Engineering, or similar technical roles.

  • Handson experience with bringup and validation of largescale NVIDIA GPU platforms, including multiGPU and multinode architectures.

  • Understanding of highperformance networking technologies (e.g., RDMA, congestion control, highbandwidth interconnects) and their role in distributed AI workloads.

  • Familiarity with NVIDIA system software stacks: CUDA, NCCL, NVSwitch/NVLink, driver behavior, and performance tuning.

  • Proficiency with Linux systems tools for identifying issues and evaluating system performance, such as: dmesg, journalctl, lspci, numactl, ethtool, iostat, perf, nvidia-smi, top/htop, ipmitool, containerlevel tooling, and related utilities.

  • Understanding of server hardware architecture, including PCIe topologies, system firmware, NUMA, BIOS/UEFI configuration, power/thermal envelopes, and memory/subsystem behavior.

  • Understanding of BMC/IPMI/Redfish for remote management, hardware health monitoring, and outofband debugging during earlystage bringup.

  • Strong Linux fundamentals across drivers, kernel subsystems, cgroups, containers, and nodelevel performance analysis.

  • Ability to identify performance bottlenecks at the cluster, node, accelerator, network, or application layer.

Ways to Stand Out from the Crowd:

  • Outstanding interpersonal skills and the ability to build clarity and direction across diverse, fast paced technical teams.

  • Knowledge of Compute and networking infrastructure (e.g., Instance types, networking primitives, highperformance communication paths etc) at Hyperscalers or Cloud Service Providers.

  • Demonstrated leadership resolving multiteam infrastructure challenges across engineering, product, and customer groups.

  • A consistent record of taking GPU or infrastructure products from pilot to highvolume deployment in large data center environments.

  • Familiarity with modern deep learning, LLM architectures, and distributed training/inference challenges at scale.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until February 9, 2026.

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.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

View All Nvidia Jobs

Apply