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Entry Level Nvidia Data Scientist Jobs (NOW HIRING)

This is an Entry-Level position in the General Professional track. Job Code: P33861 Grade: P16 Data Scientist, II Produce innovative solutions driven by exploratory data analysis from complex and ...

This is an Entry-Level position in the General Professional track. Job Code: P33861 Grade: P16 Data Scientist, II Produce innovative solutions driven by exploratory data analysis from complex and ...

This is an Entry-Level position in the General Professional track. Job Code: P33861 Grade: P16 Data Scientist, II Produce innovative solutions driven by exploratory data analysis from complex and ...

Data Scientists

Salt Lake City, UT · On-site

$75K - $105K/yr

This is an Entry-Level position in the General Professional track. Job Code: P33861 Grade: P16 Data Scientist, II Produce innovative solutions driven by exploratory data analysis from complex and ...

This is an Entry-Level position in the General Professional track. Job Code: P33861 Grade: P16 Data Scientist, II Produce innovative solutions driven by exploratory data analysis from complex and ...

... scientists who research and integrate algorithms to develop an application, software, and computer system solutions to address complex data problems Assess project requirements and develop data ...

Industry giants like Nvidia, ServiceNow, Booking.com, Goldman Sachs, AstraZeneca, and Ford Motor ... and data scientists covering other domains. What you will do * Drive marketing decisions with ...

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Entry Level Nvidia Data Scientist information

See salary details

$46K

$165K

$243.5K

How much do entry level nvidia data scientist jobs pay per year?

As of Sep 13, 2026, the average yearly pay for entry level nvidia data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What does an entry level Nvidia data scientist do?

An Entry Level Nvidia Data Scientist works on analyzing large datasets, developing machine learning models, and leveraging Nvidia's GPU technologies for data processing and AI applications. They collaborate with other engineers and researchers to optimize algorithms for performance on Nvidia hardware. Their responsibilities may include data cleaning, exploratory data analysis, model training, and helping integrate AI solutions into products or services. This role provides opportunities to learn advanced tools and techniques while contributing to cutting-edge projects in AI and deep learning.

What types of projects and data sets can an entry level Nvidia data scientist expect to work on?

As an entry-level Nvidia Data Scientist, you will likely work on projects involving large-scale data analysis, machine learning model development, and AI algorithm optimization. Common tasks include processing GPU-accelerated data, collaborating with engineering teams to test and validate models, and contributing to performance benchmarking. You'll often work with diverse data sets, such as image, video, or sensor data, and participate in cross-functional team meetings. This collaborative environment provides valuable exposure to innovative technologies and offers opportunities to expand your expertise in deep learning and high-performance computing.

What are the key skills and qualifications needed to thrive as an entry level Nvidia data scientist, and why are they important?

To thrive as an Entry Level Nvidia Data Scientist, you need a strong background in statistics, machine learning, and programming (typically Python or C++), usually supported by a relevant degree in computer science, data science, or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), GPU programming (CUDA), and data visualization tools is highly valued, along with any related certifications. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret data and collaborate across technical and business teams. These skills and qualities are vital for extracting actionable insights from complex data and contributing to innovative AI and computing solutions at Nvidia.

What is the difference between Entry Level Nvidia Data Scientist vs Entry Level Nvidia Data Analyst?

AspectEntry Level Nvidia Data ScientistEntry Level Nvidia Data Analyst
Required CredentialsBachelor's in Data Science, Computer Science, or related field; knowledge of machine learning and programmingBachelor's in Statistics, Business, or related field; proficiency in data visualization and SQL
Work EnvironmentDeveloping models, analyzing complex data, collaborating with data science teamsData reporting, cleaning, and visualization tasks within business units
Employer & Industry UsageTech companies, AI research, hardware/software developmentBusiness analytics, marketing, finance within Nvidia or partner organizations

Entry Level Nvidia Data Scientist focuses on building predictive models and applying machine learning techniques, while Entry Level Nvidia Data Analyst primarily handles data reporting and visualization. Both roles require strong analytical skills, but data scientists work more on developing algorithms, whereas data analysts focus on interpreting data for business insights.

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Infographic showing various Entry Level Nvidia Data Scientist job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Data Scientist - Fraud Detection

Mountain View, CA • On-site

DataVisor
Software Development • 1 - 10 employees

Full-time

Medical, PTO

Posted 18 days ago


Job description

About DataVisor:
DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Position Overview:
We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team. In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets. You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.
Key Responsibilities:
  • Develop and deploy machine learning models for fraud detection and risk assessment.
  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.
  • Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.).
  • Collaborate with engineering and business teams to integrate ML models into production systems.
  • Continuously monitor model performance and refine algorithms to improve accuracy.
  • Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies.

Requirements
  • Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field. Ph.D. degree is a plus.
  • Strong programming skills in Python and familiarity with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch is a plus).
  • Solid understanding of machine learning algorithms (supervised/unsupervised learning, anomaly detection, classification, etc.).
  • Experience with SQL and data manipulation/analysis in large datasets.
  • Strong problem-solving skills and patience for deep-dive data exploration.
  • Prior internship or project experience in fraud modeling, risk analysis, or related fields is a plus.
  • Excellent communication skills and ability to work in a collaborative environment.

Nice to have
  • Familiarity with big data tools (Spark, Hadoop, Dask).
  • Knowledge of graph-based fraud detection techniques.
  • Experience with cloud platforms (AWS, GCP, Azure).

Benefits
PTO, Stock Option, Health Benefits