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Machine Learning Astronomy Jobs in Georgia (NOW HIRING)

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Machine Learning Astronomy information

What is the difference between Machine Learning Astronomy vs Data Scientist?

AspectMachine Learning AstronomyData Scientist
Required CredentialsDegree in Astronomy, Physics, or related fields; knowledge of machine learningDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch institutions, observatories, academiaCorporate, tech companies, consulting firms
Industry UsageAnalyzing astronomical data, developing models for celestial phenomenaBusiness analytics, predictive modeling, data visualization

Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.

What are 5 potential jobs for astronomy?

Potential jobs for astronomy graduates include research scientist at observatories or universities, data analyst for space agencies, astrophysics researcher, science communicator or educator, and software developer for astronomical data analysis. These roles often require strong analytical skills, programming knowledge, and familiarity with telescopes or data processing tools.

How much do machine learning engineers make at NASA?

Machine learning engineers at NASA typically earn between $90,000 and $150,000 annually, depending on experience, education, and security clearance levels. Salaries may also vary based on location and specific project responsibilities, with some roles requiring expertise in data analysis, programming, and scientific computing tools.

What is machine learning astronomy?

Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.

What are the key skills and qualifications needed to thrive as a Machine Learning Astronomer, and why are they important?

To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.

Does NASA have machine learning engineers?

NASA employs machine learning engineers to develop algorithms for data analysis, spacecraft navigation, and scientific research. These roles often require expertise in programming, data science, and tools like Python and TensorFlow, with positions available through federal job portals and NASA's career website.

What are some common challenges faced by professionals working in machine learning astronomy?

Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.

Can AI replace astronomers?

Machine Learning Astronomers use AI to analyze large datasets, identify patterns, and make predictions about celestial phenomena. While AI can automate data processing and assist in research, it does not replace the need for human expertise in designing experiments, interpreting results, and making scientific judgments. The role of astronomers remains essential for guiding AI applications and advancing understanding of the universe.
What are popular job titles related to Machine Learning Astronomy jobs in Georgia? For Machine Learning Astronomy jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in Georgia look for? The top searched job categories for Machine Learning Astronomy jobs in Georgia are:
What cities in Georgia are hiring for Machine Learning Astronomy jobs? Cities in Georgia with the most Machine Learning Astronomy job openings:
Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in Georgia as of June 2026, with employment types broken down into 1% Internship, 41% Full Time, 56% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.
Data Scientist - multiple levels - CLEARANCE and POLYGRAPH REQUIRED

Data Scientist - multiple levels - CLEARANCE and POLYGRAPH REQUIRED

Constellation Technologies, Inc

Augusta, GA • On-site

$120K - $220K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago

Job description

Big Data, dataflows, Artificial Intelligence / Machine Learning (AI/ML) familiarity, Analytics in GME, Jupyter notebooks, and Spark.
 
Due to federal contract requirements, United States citizenship and an active TS/SCI security clearance and polygraph are required for the position.
 
 
Required:
  • Must be a US Citizen
  • Must have TS/SCI clearance w/ active polygraph
  • This position is open to multiple levels of years of experience; two (02) years within the last five (05) years must be directly related to the job you are applying for:
  • Level 04 requires a minimum seventeen (17) years of experience w/ Degree
  • Level 03 requires a minimum twelve (12) years of experience w/ Degree
  • Level 02 requires a minimum five (05) years of experience w/ Degree
  • Degree in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g., physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e., behavioral, social, and life) may be considered if it includes a concentration of coursework (typically 5 or more courses) in advanced mathematics (typically 300 level or higher; such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g., algorithms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper-level math courses designated as elementary or basic do not count.
  • Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python) and skill in at least one mid-level language (e.g. C)), data mining, advanced statistical analysis (e.g. statistical foundations of machine learning, statistical approaches to missing data, time series), advanced mathematical foundations (e.g. numerical methods, graph theory), artificial intelligence, workflow and reproducibility, data management and curation, data modeling and assessment (e.g. model selection, evaluation, and sensitivity.
  • Employ some combination (2 or more) of the following areas: Foundations (Mathematical, Computational, Statistical); Data Processing (Data management and curation, data description and visualization, workflow, and reproducibility); Modeling, Inference, and Prediction (Data modeling and assessment, domain-specific considerations).
  • Devise strategies for extracting meaning and value from large datasets.
  • Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge.
  • Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent to Agency data holdings.
  • Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data.
  • Effectively communicate complex technical information to non-technical audiences.
  • Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting Agency collection, processing, storage and analytic capabilities and limitations.
These Qualifications Would be Nice to Have:
  • Fully Cleared polygraph is preferred
  • Knowledge of working with Big Data, dataflows, Machine Learning/Artificial Intelligence familiarity.
  • Analytics in GME, Jupyter notebooks, and Spark.
$120,000 - $220,000 a year
The pay range for this job, with multi-levels, is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
The benefits package:
 
Affordable healthcare options with 80% employer paid premium PLUS a company-funded HSA
Dental insurance with 100% employer paid premium
Vision with 80% employer paid premium
Employer paid Life insurance 100%
Employer paid Short-term and Long-term disability 100%
Annual training, continued education, and professional memberships reimbursement
Unlimited access to Red Hat Enterprise Linux, AWS, and NetApp training and accreditation
Annual reimbursement for technology i.e. phones, computers, printers, etc...
401(k) with company match up to 5% with 100% immediate vesting (after 90 days of employment)
 
The environment and perks:
 
Professional development investment and paid time off for training
Contract and work locations in Maryland, Virginia, Colorado, Texas, Utah, California, Florida and Hawaii.
Team building events throughout the year such as Destination Family Events, Holiday Party, Monthly Get-Togethers
Leadership Team engagement and mentorship
Performance Recognition Program
Complimentary branded apparel
 
Don't see a job opening that's the perfect fit? Apply to our General Position to join our talent pool for consideration for future opportunities.
 
Know someone else who may be a good fit? Refer them through the CTI External Referral Program and you could receive a one-time referral bonus of up to $10,000! Email [email protected] for more information.
 
Constellation Technologies is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, religion, creed, color, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, or medical conditions related to pregnancy, childbirth, or breastfeeding), age, medical condition, marital or domestic partner status, sexual orientation, gender, gender identity, gender expression and transgender status, mental disability or physical disability, genetic information, military or veteran status, citizenship, low-income status or any other status or characteristic protected by applicable law. Job applicants can submit questions about CTI's equal employment opportunity policy to [email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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