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Machine Learning Astronomy Jobs in Maryland (NOW HIRING)

GRVTY

Data Scientist 2

Annapolis Junction, MD · On-site

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 ...

GRVTY

Data Scientist 3

Annapolis Junction, MD · On-site

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

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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 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.

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.

What are popular job titles related to Machine Learning Astronomy jobs in Maryland?

For Machine Learning Astronomy jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Machine Learning Astronomy jobs in Maryland look for?

The top searched job categories for Machine Learning Astronomy jobs in Maryland are:

What cities in Maryland are hiring for Machine Learning Astronomy jobs?

Cities in Maryland with the most Machine Learning Astronomy job openings:

Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in Maryland as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Scientist

Fuse Engineering LLC

Fort George G Meade, MD

Full-time

Re-posted 23 days ago

Job description

Description

Support for NLP project to accurately and automatically tokenize language data with spoken or written origins; develop automated solutions for the annotation of language data with parts of speech information, and improved existing models by scoring performance against human-generated annotations for speech and text. 

Requirements

Clearance Required

Top Secret SCI w/ Full Polygraph


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. 


Must have some combination (2 or more) of the following skill areas:  

Foundations: Mathematical, Computational, Statistical

Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least on 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.  

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