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Machine Learning Geospatial Jobs in Virginia (NOW HIRING)

BAE Systems

Imagery Anst Sr

Falls Church, VA · On-site

$97K - $164K/yr

Responsibilities for this position include providing imagery and geospatial analysis services in the Machine Learning disciplines to facilitate the creation, review, and verification of training data ...

BAE Systems

Imagery Anst II

Falls Church, VA · On-site

$79K - $134K/yr

Responsibilities for this position include providing imagery and geospatial analysis services in the Machine Learning disciplines to facilitate the creation, review, and verification of training data ...

BAE Systems

Imagery Anst Sr

Falls Church, VA · On-site

$95K - $161K/yr

Responsibilities for this position include providing imagery and geospatial analysis services in the Machine Learning disciplines to facilitate the creation, review, and verification of training data ...

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Machine Learning Geospatial information

What does a Machine Learning Geospatial professional do?

A Machine Learning Geospatial professional uses machine learning techniques to analyze and interpret geospatial data, such as satellite imagery, maps, and GPS data. Their work involves building and training models to detect patterns, make predictions, and solve spatial problems in fields like agriculture, urban planning, disaster response, and environmental monitoring. These professionals often collaborate with data scientists and GIS (Geographic Information Systems) specialists to extract actionable insights from large and complex geospatial datasets. Their skills are crucial for automating tasks such as image classification, land cover mapping, and object detection in geographic contexts.

What are some common challenges faced by Machine Learning Geospatial professionals when integrating spatial data into predictive models?

Machine Learning Geospatial professionals often encounter challenges such as managing large and complex spatial datasets, ensuring data quality and consistency, and handling spatial autocorrelation that can bias model results. Additionally, integrating diverse data sources—like satellite imagery, sensor data, and GIS layers—requires advanced pre-processing and domain knowledge. Collaborating with GIS analysts and domain experts is usually essential to develop robust models that provide actionable insights.

What is the difference between Machine Learning Geospatial vs GIS Analyst?

AspectMachine Learning GeospatialGIS Analyst
Required CredentialsBachelor's or higher in Computer Science, Data Science, or related fields; knowledge of machine learning and geospatial dataBachelor's in Geography, GIS, or related fields; proficiency in GIS software
Work EnvironmentTech companies, data science teams, research institutionsGovernment agencies, urban planning, environmental firms
Industry UsageData-driven geospatial analysis, predictive modeling, AI applicationsMapping, spatial data management, spatial analysis

Machine Learning Geospatial professionals focus on applying machine learning techniques to analyze geospatial data, often working with large datasets and developing predictive models. GIS Analysts primarily handle spatial data management, mapping, and analysis using GIS software. While both roles work with geospatial data, Machine Learning Geospatial roles emphasize data science and AI, whereas GIS Analysts focus on spatial information management and visualization.

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

To thrive as a Machine Learning Geospatial specialist, you need a strong background in machine learning, geospatial analysis, programming (Python, R), and a relevant degree in computer science, geography, or a related field. Familiarity with GIS software (e.g., ArcGIS, QGIS), remote sensing tools, and cloud platforms like Google Earth Engine or AWS is typically required. Analytical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with multidisciplinary teams. These skills and qualities are crucial for developing accurate geospatial models and delivering actionable insights from complex spatial data.
What are popular job titles related to Machine Learning Geospatial jobs in Virginia? For Machine Learning Geospatial jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Geospatial jobs in Virginia look for? The top searched job categories for Machine Learning Geospatial jobs in Virginia are:
What cities in Virginia are hiring for Machine Learning Geospatial jobs? Cities in Virginia with the most Machine Learning Geospatial job openings:
Machine Learning Geospatial jobs near you
Senior Data Scientist (Russian Geospatial Linguist) - TS/SCI Required

Senior Data Scientist (Russian Geospatial Linguist) - TS/SCI Required

Leidos

Springfield, VA • On-site

$92K - $166K/yr

Full-time

Posted 6 days ago

Leidos rating

8.4

Company rating: 8.4 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

56th of 425 rated business services

Job description

Leidos is seeking a Senior Analyst with Russian Geospatial Linguistic expertise to support a customer site in Springfield, VA.
An active TS/SCI clearance with willingness to obtain a Polygraph is required to be considered.
This position is a unique hybrid role requiring deep expertise in Imagery Analysis (IMINT/GEOINT) combined with advanced data science capabilities and strong Russian language and geospatial linguistics/toponymy skills. The selected candidate will exploit, analyze, and produce intelligence products integrating foreign geographic names data, native-language sources, and geospatial metadata to enhance analytic accuracy and mission impact.
The analyst will work closely with Intelligence Community (IC) partners, geographic names experts, and multi-INT analysts to ensure produced data is geospatially precise, linguistically accurate, and operationally relevant.
Accuracy, analytic rigor, and mission responsiveness are essential.
Primary Responsibilities
  • Structure disparate and unstructured data (imagery, text, geospatial features) into usable formats for quantitative analysis and fusion.
  • Develop and maintain data pipelines using Python, R, or SQL to support automated analytic workflows.
  • Apply existing machine learning models and statistical methods to support pattern detection, feature extraction, and predictive analysis.
  • Develop ontologies and data schemas to standardize geospatial and linguistic datasets across analytic environments.
  • Generate automated workflows to improve efficiency, reproducibility, and scalability of analytic production.
  • Perform exploratory and ad hoc data analysis to identify trends, anomalies, and mission-relevant insights.
  • Aggregate existing data stores and enable natural language processing (NLP) query capabilities using existing APIs.
  • Process and analyze large volumes of unstructured data and documents to extract mission-relevant insights.
  • Integrate geospatial and Russian-language data sources to enhance analytic context and accuracy.
  • Translate complex quantitative and analytic findings into clear, actionable intelligence through visualization and storytelling.
  • Collaborate with cross-functional analytic teams to ensure consistency in data, language, and geospatial standards.
  • Brief findings clearly and confidently to technical and non-technical audiences.

Basic Qualifications
  • Active TS/SCI clearance with willingness to obtain a Polygraph.
  • Bachelor's degree and 12+ years of relevant experience, or Master's degree and 10+ years of relevant experience in Data Science, Analytics, GEOINT, or related field. Additional experience may be considered in lieu of degree.
  • Demonstrated senior-level experience applying data science methodologies (machine learning, statistical analysis, data engineering) to real-world problems.
  • Proficiency in Python, R, or SQL for data analysis, pipeline development, and automation.
  • Experience structuring and processing large, complex, and unstructured datasets.
  • Experience processing unstructured data and documents (e.g., text, reports, open-source content).
  • Experience developing or supporting NLP capabilities, including querying across aggregated data sources.
  • Russian language proficiency with minimum ILR Level 2+ reading (3 preferred).
  • Strong understanding of geospatial data concepts and ability to integrate spatial data into analytic workflows.
  • Experience working with GIS tools such as ArcGIS Pro or QGIS.
  • Strong research, critical thinking, and analytic writing skills.
  • Experience operating effectively in fast-paced, mission-driven environments both independently and as part of a team.
  • Familiarity with working in high-side (classified) environments.

Desired Qualifications
  • Regional expertise in Russian Federation and/or Russian-influenced geographies.
  • Experience developing machine learning models or AI-enabled analytics within national security or GEOINT environments.
  • Proficiency in Python for advanced analytics, automation, or data engineering workflows.
  • Experience developing ontologies, schemas, or knowledge graphs for geospatial or linguistic data.
  • Experience integrating multi-INT or multi-source data into advanced analytic workflows.
  • Prior experience briefing senior government stakeholders.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.
Original Posting:
April 2, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
Pay Range $92,300.00 - $166,850.00
The Leidos pay range for this job level 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.

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About Leidos

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At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable practices. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Reston, VA, US

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