1

Spatial Data Science Jobs in Washington (NOW HIRING)

Sr. GIS Solution Engineer- Health & Sciences

Vienna, VA · On-site

$55.50 - $71.50/hr

Be a technical leader of Esri technology as well as a subject matter expert of spatial data science. Demonstrate your advanced understanding of sales strategies and initiatives to develop complex ...

Spatial Front, Inc. (SFI) is a recognized workplace seeking a Data Scientist to enhance their team. The role involves developing analytical models and machine learning solutions to provide insights ...

Spatial Front, Inc. is seeking a Data Scientist to support our growing team. The ideal candidate will develop advanced analytical models and machine learning solutions that generate actionable ...

Data Architect

Arlington, VA · On-site

$73.25 - $94.25/hr

Design and maintain enterprise data architectures that support geospatial operations, spatial ... Master's degree in Computer Science, Information Systems, Data Science, Geospatial Science ...

With experts in biomedical science, software engineering, and program management, we focus on ... Support figure generation for QC, differential expression, pathway, and spatial analyses.

next page

Showing results 1-20

Spatial Data Science information

See Washington salary details

$50.4K

$146.9K

$201K

How much do spatial data science jobs pay per year?

As of Aug 30, 2026, the average yearly pay for spatial data science in Washington is $146,916.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,700.00 and $155,700.00 per year, depending on experience, location, and employer.

What is spatial data science?

Spatial data science is a field that combines data science techniques with geographic information systems (GIS) to analyze and interpret spatial or location-based data. It involves collecting, processing, and visualizing data that has a geographic or spatial component, such as maps, satellite images, or GPS coordinates. Spatial data scientists use methods from statistics, machine learning, and computer science to solve problems related to urban planning, environmental monitoring, transportation, and more. The insights gained from spatial data science help organizations make better decisions based on the relationships and patterns found in geographic data.

What are the key skills and qualifications needed to thrive as a spatial data scientist, and why are they important?

To thrive as a Spatial Data Scientist, you need a strong background in statistics, geospatial analysis, and programming (often with Python or R), typically supported by a degree in geography, computer science, or a related field. Proficiency with GIS software (such as ArcGIS or QGIS), spatial databases (like PostGIS), and relevant certifications (e.g., Esri Technical Certification) is commonly required. Strong analytical thinking, problem-solving abilities, and effective communication are vital soft skills to interpret spatial data and convey insights to stakeholders. These competencies are crucial for extracting actionable insights from complex geospatial datasets and supporting informed decision-making.

What are some typical challenges spatial data scientists face when integrating geospatial data from multiple sources?

Spatial data scientists often encounter challenges like inconsistencies in data formats, varying coordinate reference systems, and differences in spatial resolution when integrating geospatial data from multiple sources. Addressing these requires familiarity with data transformation tools and a strong understanding of spatial data standards. Additionally, ensuring data quality and managing large datasets can be complex, so attention to detail and effective use of GIS software are crucial for successful integration.

What is the difference between Spatial Data Science vs Geospatial Analyst?

AspectSpatial Data ScienceGeospatial Analyst
Required CredentialsDegree in GIS, Geography, Data Science, or related fields; often includes certifications in GIS or data analysisDegree in Geography, GIS, or related fields; certifications in GIS software are common
Work EnvironmentData analysis, modeling, and programming; often in tech or research settingsMapping, data visualization, and GIS software use; typically in government, environmental, or urban planning agencies
Employer & Industry UsageTech companies, research institutions, urban planning, environmental agenciesGovernment agencies, environmental consultancies, urban planning firms

Spatial Data Science focuses on analyzing spatial data using advanced data science techniques, programming, and modeling. In contrast, Geospatial Analysts primarily work with GIS software to create maps and visualize spatial data. While both roles require GIS knowledge, Spatial Data Scientists often have stronger programming and statistical skills, working on complex data analysis projects, whereas Geospatial Analysts focus more on mapping and data visualization tasks.

What does a spatial data scientist do?

A spatial data scientist analyzes geographic data to identify patterns, relationships, and trends using tools like GIS software and programming languages such as Python or R. They develop models, visualize spatial information, and support decision-making in fields like urban planning, environmental management, and logistics.

What are popular job titles related to Spatial Data Science jobs in Washington?

For Spatial Data Science jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Spatial Data Science jobs in Washington look for?

The top searched job categories for Spatial Data Science jobs in Washington are:

What cities in Washington are hiring for Spatial Data Science jobs?

Cities in Washington with the most Spatial Data Science job openings:

Infographic showing various Spatial Data Science job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $146,916 per year, or $70.6 per hour.

Mid-level Data Scientist (OBI Advanced Analytic Method Augmentation) - OBIQUA

CELESTAR

Reston, VA • On-site

Full-time

Medical, Dental, Life, Retirement, PTO

Re-posted 17 days ago


Job description

Celestar Corporation is seeking a Mid-level Data Scientist (OBI Advanced Analytic Method Augmentation) to support The Defense Intelligence Agency (DIA) under the Object Based Intelligence and Quality Assurance (OBIQUA) task order. The primary place of performance will be at DIA Facilities across the National Capital Region (NCR). If interested and meet the qualifications, we encourage you to apply for this rewarding and impactful opportunity.
ANTICIPATED AWARD: TBD
ANTICIPATED START: TBD
PERIOD OF PERFORMANCE: 1 Base Year + 4 Option Years
LOCATION: DIA Facilities across the National Capital Region (NCR)
CLEARANCE REQUIREMENT: Active TS/SCI with a Current CI Poly
About Us:
Celestar, a proud Veteran-Owned company, offers highly competitive salaries and benefits. Our comprehensive benefits package includes company-paid employee and family dental insurance, employee health insurance, life insurance, and disability coverage. Additionally, we provide a 401(k)-retirement plan with company matching, paid holidays, and personal time off.
Responsibilities:
This opportunity will support multiple DIA initiatives, including the Machine-Assisted Rapid-Repository System (MARS), Object Management Services (OMS), and Object-Based Intelligence (OBI).
• The Data Scientist (OBI Advanced Analytic Method Augmentation), Conducts data analytics, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis, and uses scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions.
• Proactively retrieves information from various sources, analyzes it for better understanding about the data set, and builds AI tools that automate certain processes.
• Duties typically include: creating various ML-based tools or processes, such as recommendation engines or automated lead scoring systems.
• Performs statistical analysis, applies data mining techniques, and builds high quality prediction systems.
• Should be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as Rand Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
• Should have prior experience with large data multi-INT analytics, ML, and automated predictive analytics.
• Designs, develops, and evaluates leading-edge algorithmic intelligence concepts, practices, and technologies for implementation into all-source analysis tradecraft, assessments, production, and dissemination.
• Proposes advanced statistical or mathematical techniques and methodology that may permit identification and evaluation of alternatives, assists in model formulation or experimental test design, and shares jointly in team responsibility for development of advanced analytic techniques and assessments.
• Evaluates data science, artificial intelligence, and other advanced analytic methods for risks, biases, and limitations that would distort conclusions.
• Conducts continuous independent research on methods of analysis in government, industry, and academia to keep abreast of the state of the art, keeps senior leadership apprised of the advances and applicability to programs.
• Utilizes in-depth knowledge of relevant theories, techniques, procedures and processes to investigate, prototype, and evaluate technologies to improve all-source intelligence analysis.
• Provides technical input into and participates in the development of software and computer graphics systems.
• Performs research studies to understand the process of augmenting or automating all source analytic processes using various computer models.
• Provides incremental enhancements to tools, capabilities, processes, and methods.
• Possesses in-depth knowledge and experience in using data analytics, data engineering, data mining, exploratory analysis, predictive analysis, and statistical analysis, and scientific techniques to correlate data into graphical, written, visual and verbal narrative products, enabling more informed analytic decisions.
• Writes either R or Python scripts to drive data science workflows, have experience using SQL, and managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
• Possesses prior experience with large data, spatial data, multi-INT analytics, ML, and automated predictive analytics.
• Works with ambiguous information, deconstruct key questions, leverage spatial data, exploit application programming interfaces, suggest methodologies, develop data schemas to structure observations. This requires working knowledge of coding and scripting, information science, mathematics, machine learning, visual analytic modeling tools, and relevant Standard Operating Procedures (SOPs) to create repeatable, widely applicable procedures to support all-source intelligence analysis and production.
• Creates and works in distributed analytic environments, scaling algorithms to work on increasingly large and complex datasets that are larger than RAM.
• Serves as the primary POC for data science expertise, ensuring tradecraft compliance and analytic standards as it relates to data science techniques on the contract.
• Provides advice on emerging data science methods, tools, algorithms, training, or requirements to advance DIA's analytic edge in its use of data science.
• Works with DIA vendors and the software developers to implement distributed algorithms to work on increasingly large and complex data sets.
• Review and evaluate OBI documentation submitted by advanced analytic (AA) owners to ensure compliance with tradecraft standards and adherence to best practices in AI system development and deployment.
• Assess OBI documentation for completeness, accuracy, and thoroughness, and provide detailed feedback to owners and developers.
• Provide consultation and guidance to data and AA owners, developers, and stakeholders on OBI governance and knowledge modeling, including best practices for system development, testing, and deployment.
• Assist analytic methodologists and AA owners in translating technical documentation into analytic tradecraft compliant language.
• Collaborate with stakeholders to develop, implement, and refine best practices for translating technical documentation into tradecraft compliant language
• Review and edit translated documentation to ensure accuracy, completeness, and adherence to tradecraft standards.
• Collaborate with the Computer Scientist to develop and implement testing methodologies for system validation and evaluation.
• Conduct audits to ensure compliant use of systems for approved use-cases in all source analysis.
• Develop and maintain a repository of audit findings and recommendations to facilitate knowledge sharing and best practices across the organization.
• Design and execute TEVV protocols to evaluate the performance, robustness, and fairness of systems in all source analysis contexts.
• Develop and apply statistical models and methods to analyze TEVV results and identify areas for improvement.
• Collaborate with stakeholders to develop and implement corrective actions to address TEVV findings.
• Develop and track performance metrics to evaluate the effectiveness of systems in all source analysis.
• Analyze and interpret performance metrics to identify trends, patterns, and areas for improvement.
• Collaborate with stakeholders to develop and implement data-driven decision-making processes to inform system development and improvement.
• Develop and refine methodologies for evaluating system performance, robustness, and fairness in all source analysis contexts.
• Collaborate with stakeholders to develop and implement best practices for system development, testing, and deployment.
• Supports capability development by contributing, editing, and storing code in Government owned/controlled source version control repositories.
Required qualifications/skills:
• Minimum 8 years of experience related to the specific labor category with at least a portion of the experience within the last 2 years with a bachelor's degree.
Come on board with a company that Values its Employees!
Celestar Corporation is an Equal Opportunity Employer. The Celestar Corporation prohibits discrimination, harassment, and retaliation in employment based on race; color; religion; genetic information; national origin; sex (including same-sex); sexual orientation; gender identity; pregnancy, childbirth, or related medical conditions; age; disability or handicap; citizenship status; marital status; service member/protected veteran status; or any other category protected by federal, state, or local law.