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Machine Learning Astronomy Jobs in Washington (NOW HIRING)

... Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Data Scientist 2

Annapolis, MD · On-site

$115K - $145K/yr

This role combines artificial intelligence and machine learning skills with a strong foundation in ... astronomy), or other science disciplines with a substantial computational component (i.e ...

This role combines artificial intelligence and machine learning skills with a strong foundation in ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Data Scientist 3

Annapolis, MD · On-site

$157K - $215K/yr

Job Brief Data Science, Machine Learning Are you VIGILANT about your career? RealmOne definitely is ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Data Scientist 4

Annapolis, MD · On-site

$212K - $267K/yr

... astronomy), or other science disciplines with a substantial computational component (i.e ... Relevant experience must be in designing/implementing machine learning, data science, advanced ...

Data Scientist 2

Annapolis, MD · On-site

$122K - $168K/yr

You will be providing advanced discovery support utilizing machine learning, analytical prototyping ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Data Scientist 2

Annapolis, MD · On-site

$122K - $168K/yr

You will also provide advanced discovery support using machine learning, analytical prototyping ... astronomy), or other science disciplines with a substantial computational component (i.e ...

Showing results 21-40

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 Washington? For Machine Learning Astronomy jobs in Washington, the most frequently searched job titles are:
Infographic showing various Machine Learning Astronomy job openings in Washington as of June 2026, with employment types broken down into 2% As Needed, 37% Full Time, 56% Part Time, 2% Temporary, and 3% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.

Data Scientist 3 with Security Clearance

GRVTY

Annapolis, MD • On-site

Other

This job post has expired today. Applications are no longer accepted.


Job description

What You'll be Owning: * We are seeking a Data Scientist to support our NLP project focused on accurate and automatic tokenization of language data from spoken or written sources. In this role, you will develop automated solutions for annotating language data with parts of speech information and enhance existing models by evaluating their performance against human-generated annotations for both speech and text. Your contributions will be crucial in advancing our NLP capabilities and ensuring high-quality language processing. What You Must Have : * Bachelor's Degree with 10 years of relevant experience * Associates degree with 12 years of relevant experience * Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (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, or life) may be considered if it included a concentration of coursework (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 requirement, or upper-level math courses designated as elementary or basic do not count. Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. * 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)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred. * Active TS/SCI w/ poly What Would Be Nice to Have: * Employ some combination (2 or more) of the following skill 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 in Government 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 Government collection, processing, storage and analytic capabilities and limitations. #LI-SM1