ZipRecruiter

Log In

2

Entry Level Astronomy Data Analysis Jobs (NOW HIRING)

aui

Software Engineer III - UPDATED

Socorro, NM · On-site +1

$54.25 - $72.75/hr

This role will prototype, develop, benchmark and optimize the Radio Astronomy Data Processing ... analysis and algorithmic design through implementation, testing, optimization, and long-term ...

aui

Software Engineer III - UPDATED

Albuquerque, NM · On-site +1

$53.75 - $72/hr

This role will prototype, develop, benchmark and optimize the Radio Astronomy Data Processing ... analysis and algorithmic design through implementation, testing, optimization, and long-term ...

Varsity Tutors

Astronomy Tutor

AZ · Remote

$40/hr

What We Look For In a Astronomy Tutor * Advanced Subject Mastery: Deep knowledge of celestial ... analyzing light curves, and understanding redshift data. Emphasizes quantitative reasoning about ...

Varsity Tutors

Astronomy Tutor

Stillwater, OK · Remote

$40/hr

What We Look For In a Astronomy Tutor * Advanced Subject Mastery: Deep knowledge of celestial ... analyzing light curves, and understanding redshift data. Emphasizes quantitative reasoning about ...

next page

Showing results 1-20

People also search for

All JobsEntry Level Astronomy Data Analysis Jobs

Entry Level Astronomy Data Analysis information

See salary details

$13

$32

$61

How much do entry level astronomy data analysis jobs pay per hour?

As of Jun 1, 2026, the average hourly pay for entry level astronomy data analysis in the United States is $32.93, according to ZipRecruiter salary data. Most workers in this role earn between $21.15 and $36.78 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level Astronomy Data Analyst, and why are they important?

To thrive as an Entry Level Astronomy Data Analyst, you need a solid background in physics, mathematics, and astronomy, usually supported by a bachelor’s degree in a related field. Familiarity with data analysis tools such as Python, MATLAB, or R, as well as experience with astronomical databases and software like IRAF or Astropy, is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret complex data and collaborate with research teams. These skills are crucial for accurately analyzing astronomical datasets and contributing to scientific discoveries.

What are some common challenges faced by entry-level professionals in astronomy data analysis, and how can they overcome them?

Entry-level professionals in astronomy data analysis often encounter challenges such as interpreting complex datasets, mastering specialized software, and staying updated with rapidly evolving analytical techniques. To overcome these hurdles, it's helpful to actively seek guidance from senior team members, participate in training sessions, and engage with the broader astronomy data analysis community. Collaborating closely with astronomers, data scientists, and software engineers within your team can also accelerate your learning and help you develop effective problem-solving strategies.

What is entry level astronomy data analysis?

Entry level astronomy data analysis involves processing and interpreting astronomical data collected from telescopes and other instruments. Individuals in these positions typically assist with tasks such as calibrating data, identifying patterns, and supporting research projects under the guidance of senior astronomers. They often use software tools for statistical analysis, image processing, and database management. This role provides foundational experience in the field and is suited for those with a background in physics, astronomy, or a related discipline.

What is the difference between Entry Level Astronomy Data Analysis vs Entry Level Astrophysics Research Assistant?

AspectEntry Level Astronomy Data AnalysisEntry Level Astrophysics Research Assistant
Required CredentialsBachelor's in Astronomy, Physics, or related fieldBachelor's in Physics, Astronomy, or related field
Work EnvironmentData centers, observatories, research institutionsResearch labs, observatories, universities
Employer & Industry UsageResearch institutions, space agencies, universitiesUniversities, research institutes, government agencies
Common Search & ComparisonYesYes

Entry Level Astronomy Data Analysis focuses on processing and interpreting astronomical data using software tools, while Entry Level Astrophysics Research Assistant involves supporting astrophysics research projects, often including data analysis, literature review, and experimental setup. Both roles require similar educational backgrounds and are found in research environments, but their specific tasks differ slightly.

More about Entry Level Astronomy Data Analysis jobs
What cities are hiring for Entry Level Astronomy Data Analysis jobs? Cities with the most Entry Level Astronomy Data Analysis job openings:
What are the most commonly searched types of Astronomy Data Analysis jobs? The most popular types of Astronomy Data Analysis jobs are:
What job categories do people searching Entry Level Astronomy Data Analysis jobs look for? The top searched job categories for Entry Level Astronomy Data Analysis jobs are:
Entry Level Astronomy Data Analysis jobs near you
Infographic showing various Entry Level Astronomy Data Analysis job openings in the United States as of May 2026, with employment types broken down into 90% Full Time, and 10% Part Time. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $68,487 per year, or $32.9 per hour.

Undergraduate Intern - Quantitative Data & Finance (Cross-Disciplinary

Risk Analytics Company

Cambridge, MA

Full-time

Posted 27 days ago

Job description

Position Overview
We are seeking an undergraduate student to support our Finance and IT teams with a focus on data analysis and quantitative problem-solving. This internship provides hands-on experience working with financial datasets, basic modeling, and tools used in data-driven decision-making.
We welcome candidates from analytical and cross-disciplinary backgrounds—including mathematics, applied mathematics, statistics, economics, engineering, computer science, physics, astrophysics, quantum computing, biotech and other data-driven fields—who are interested in applying quantitative thinking to real-world business and financial problems.
Key Responsibilities
  • Work with structured datasets to support basic financial and operational analysis 
  • Assist in organizing, cleaning, and validating data for reporting and modeling 
  • Build and maintain spreadsheets and simple analytical models in Excel 
  • Support development of reports, dashboards, and visualizations 
  • Identify patterns, inconsistencies, or trends in data 
  • Assist with automation or efficiency improvements using tools like Excel, SQL, or Python (where applicable) 
  • Collaborate with team members on data, finance, and technology-related tasks 
  • Apply quantitative or analytical approaches from coursework to practical business problems 
Required Qualifications
  • Currently pursuing a Bachelor’s degree in a quantitative or analytical field (e.g., Mathematics, Applied Mathematics, Physics, Astrophysics, Statistics, Economics, Computer Science, Quantum Computing, Engineering, Finance, Biotech, or other data-driven discipline) 
  • Strong problem-solving and analytical thinking skills 
  • Familiarity with: 
    • Microsoft Excel (formulas, basic functions) 
    • Microsoft Office (Word, PowerPoint) 
  • Comfort working with numbers and structured data 
  • Strong attention to detail and willingness to learn 
Preferred Qualifications
  • Exposure to programming or data tools (Python, SQL, R, or similar) through coursework or projects 
  • Experience with Excel functions (e.g., VLOOKUP, pivot tables) 
  • Introductory knowledge of statistics, probability, or data analysis 
  • Interest in financial markets, data analytics, or fintech 
  • Coursework or projects involving: 
    • Data analysis or visualization
  • Mathematical modeling 
  • Machine learning (introductory) 
  • Computational or applied problem-solving 
What You’ll Gain
  • Hands-on experience applying quantitative skills in a real-world business environment 
  • Exposure to financial data, analytics workflows, and decision-making processes 
  • Opportunity to build foundational data and modeling skills 
  • Mentorship and professional development 
  • Insight into career paths in quantitative finance, data science, and analytics 
Internship Details
  • Summer 2026, with possible extensionÂ