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Intern Nvidia Data Scientist Jobs in Georgia (NOW HIRING)

$35/hr

As an intern, you won't just observe -- you'll contribute to meaningful projects, gain exposure to ... Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ...

$35/hr

As an intern, you won't just observe -- you'll contribute to meaningful projects, gain exposure to ... Opportunities Available As a Real-World Evidence Data Science intern at Stryker, you will: * Work ...

The data science intern will help drive proactive and predictive insights that inform strategic decision-making across the Commercial Excellence organization within the Trauma & Extremities division.

$35/hr

The data science intern will help drive proactive and predictive insights that inform strategic decision-making across the Commercial Excellence organization within the Trauma & Extremities division.

The data science intern will help drive proactive and predictive insights that inform strategic decision-making across the Commercial Excellence organization within the Trauma & Extremities division.

The data science intern will help drive proactive and predictive insights that inform strategic decision-making across the Commercial Excellence organization within the Trauma & Extremities division.

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Intern Nvidia Data Scientist information

What does an intern Nvidia data scientist do?

An Intern Nvidia Data Scientist works with experienced teams to analyze large datasets, develop models, and create data-driven solutions using machine learning and deep learning techniques. The role often includes tasks like data cleaning, exploratory analysis, model training, and performance evaluation. Interns may also help optimize algorithms for Nvidia hardware platforms and contribute to real-world projects that impact Nvidia’s products and services. This experience provides interns with valuable exposure to cutting-edge AI technologies and industry best practices.

What types of projects can an intern Nvidia data scientist expect to work on?

As a Data Scientist intern at Nvidia, you can expect to work on a variety of projects that leverage large datasets and advanced machine learning techniques. Typical assignments may involve developing models for computer vision, natural language processing, or GPU-accelerated data analytics. You'll likely collaborate closely with experienced scientists, software engineers, and product teams, contributing to both research-oriented and production-focused tasks. These projects offer valuable exposure to real-world challenges and cutting-edge technologies, providing an excellent foundation for future career growth in the field.

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

To thrive as an Intern Nvidia Data Scientist, you need a strong background in statistics, machine learning, programming (Python, R), and a relevant degree in computer science, data science, or a related field. Familiarity with tools like TensorFlow, PyTorch, CUDA, and data visualization software is often expected. Strong problem-solving abilities, communication skills, and a collaborative mindset help you stand out in this role. These competencies are crucial for successful project contributions, effective teamwork, and leveraging Nvidia's advanced technologies in real-world data science applications.

What is the difference between Intern Nvidia Data Scientist vs Intern Nvidia Data Analyst?

AspectIntern Nvidia Data ScientistIntern Nvidia Data Analyst
Required CredentialsRelevant degree in Data Science, Computer Science, or related field; programming skills in Python, R; basic understanding of machine learningDegree in Data Analysis, Statistics, or related; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams focusing on advanced analytics, machine learning models, and AI projectsData reporting, visualization, and interpreting data trends for business insights
Employer & Industry UsageUsed across Nvidia's AI, autonomous vehicles, and research divisionsApplied in marketing, sales, and product teams for data-driven decision making

Intern Nvidia Data Scientists focus on developing machine learning models and advanced analytics, requiring programming and statistical skills. Intern Nvidia Data Analysts primarily interpret data and create reports, emphasizing data visualization and business insights. Both roles are valuable in Nvidia's tech environment but differ in technical depth and project scope.

What are the most commonly searched types of Nvidia Data Scientist jobs in Georgia?

The most popular types of Nvidia Data Scientist jobs in Georgia are:

What cities in Georgia are hiring for Intern Nvidia Data Scientist jobs?

Cities in Georgia with the most Intern Nvidia Data Scientist job openings:

Infographic showing various Intern Nvidia Data Scientist job openings in Georgia as of August 2026, with employment types broken down into 42% Internship, and 58% Full Time. Highlights an 100% In-person job distribution.

$49K/yr

Full-time, Part-time

Re-posted 2 days ago


Job description

The PALACE Acquire Program offers you a permanent position upon completion of your formal training plan. As a Palace Acquire Intern you will experience both personal and professional growth while dealing effectively and ethically with change, complexity, and problem solving. The program offers a 3-year formal training plan with yearly salary increases. Promotions and salary increases are based upon your successful performance and supervisory approval.Qualifications:BASIC REQUIREMENT OR INDIVIDUAL OCCUPATIONAL REQUIREMENT:
Degree: Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.
You may qualify if you meet one of the following:
1. GS-7: You must have completed or will complete a 4-year course of study leading to a bachelor's from an accredited institution AND must have documented Superior Academic Achievement (SAA) at the undergraduate level in the following:
a) Grade Point Average 2.95 or higher out of a possible 4.0 as recorded on your official transcript or as computed based on 4 years of education or as computed based on courses completed during the final 2 years of curriculum; OR 3.45 or higher out of a possible 4.0 based on the average of the required courses completed in your major field or the required courses in your major field completed during the final 2 years of your curriculum.
2. GS-9: You must have completed 2 years of progressively higher-level graduate education leading to a master's degree or equivalent graduate degree:
a) Grade Point Average - 2.95 or higher out of a possible 4.0 as recorded on your official transcript or as computed based on 4 years of education or as computed based on courses completed during the final 2 years of curriculum; OR 3.45 or higher out of a possible 4.0 based on the average of the required courses completed in your major field or the required courses in your major field completed during the final 2 years of your curriculum. If more than 10 percent of total undergraduate credit hours are non-graded, i.e. pass/fail, CLEP, CCAF, DANTES, military credit, etc. you cannot qualify based on GPA.
KNOWLEDGE, SKILLS AND ABILITIES (KSAs): Your qualifications will be evaluated on the basis of your level of knowledge, skills, abilities and/or competencies in the following areas:
1. Professional knowledge of basic principles, concepts, and practices of data science to apply scientific methods and techniques to analyze systems, processes, and/or operational problems and procedures.
2. Knowledge of mathematics and analysis to perform minor phases of a larger assignment and prepare reports, documentation, and correspondence to communicate factual and procedural information clearly.
3. Skill in applying basic principles, concepts, and practices of the occupation sufficient to perform routine to difficult but well precedented assignments in data science analysis.
4. Ability to analyze, interpret, and apply data science rules and procedures in a variety of situations and recommend solutions to senior analysts.
5. Ability to analyze problems to identify significant factors, gather pertinent data, and recognize solutions.
6. Ability to plan and organize work and confer with co-workers effectively.
PART-TIME OR UNPAID EXPERIENCE: Credit will be given for appropriate unpaid and or part-time work. You must clearly identify the duties and responsibilities in each position held and the total number of hours per week.
VOLUNTEER WORK EXPERIENCE: Refers to paid and unpaid experience, including volunteer work done through National Service Programs (i.e., Peace Corps, AmeriCorps) and other organizations (e.g., professional; philanthropic; religious; spiritual; community; student and social). Volunteer work helps build critical competencies, knowledge and skills that can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience.Education:IF USING EDUCATION TO QUALIFY: If position has a positive degree requirement or education forms the basis for qualifications, you MUST submit transcriptswith the application. Official transcripts are not required at the time of application; however, if position has a positive degree requirement, qualifying based on education alone or in combination with experience, transcripts must be verified prior to appointment. An accrediting institution recognized by the U.S. Department of Education must accredit education. Click here to check accreditation.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying.Employment Type: OTHER