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Entry Level Nasa Data Scientist Jobs (NOW HIRING)

University of Utah

Data Scientists

Campus, IL

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 ...

University of Utah

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 ...

University of Utah

Data Scientists

Campus, IL

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 ...

University of Utah

Data Scientists

Campus, IL

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 ...

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Entry Level Nasa Data Scientist information

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

As of Aug 8, 2026, the average yearly pay for entry level nasa 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 are the most commonly searched types of Nasa Data Scientist jobs? The most popular types of Nasa Data Scientist jobs are:
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Entry Level Nasa Data Scientist jobs near you
Infographic showing various Entry Level Nasa Data Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Jr. Data Scientist 00021

West Coast Consulting LLC

Westbrook, ME • On-site

Other

Medical, Life

Re-posted 19 hours ago

Job description

Job Description
Hybrid -Westbrook, ME
Job Description:
The Machine Intelligence team in R&D is looking for an entry-level Data Scientist to develop machine learning solutions for the hematology analyzers. In this role, you will work on classification and clustering problems on tabular data, with solutions deployed on edge hardware in our analyzer platforms. You will work under the supervision of a senior data scientist who will guide your technical development and project execution. We are looking for a curious, adaptable team player eager to build foundational skills in applied machine learning.
What you can expect:
Develop classification and clustering models on tabular data to support hematology analyzer capabilities
Contribute to model development, evaluation, and iteration under the guidance of a senior data scientist
Partner with senior team members to understand requirements, explore data, and validate model performance
Document your work clearly so it can be reviewed, reproduced, and built upon by the team
Deploy your solutions to edge hardware
What you need to succeed:
0-2 years of experience applying machine learning to real-world problems (internships, research, and coursework projects count)
Strong working knowledge of Python and common data science libraries (pandas, scikit-learn, NumPy)
Solid foundation in statistics, machine learning, and algorithms
Demonstrated understanding of classification and clustering methods for tabular data, including when to apply which approach and how to evaluate results
Curiosity about the data and the underlying generating processes - a habit of asking "why" before reaching for a model
A growth mindset and willingness to learn from more senior team members
Ability to communicate analyses and results clearly to your immediate team
Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus
Nice to have:
Exposure to deploying ML models on resource-constrained or edge hardware
Familiarity with model optimization techniques (quantization, ONNX, TFLite)
Experience with version control (Git) and collaborative software development practices
Experience modeling data for medical, diagnostic or life sciences applications

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