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Spatial Data Science Jobs in Texas (NOW HIRING)

Bachelor's degree in Forestry, Environmental Science, Data Science, Statistics, Computer Science ... Experience with GIS tools and spatial data analysis. * Understanding of data governance and ...

Bachelor's degree in Forestry, Environmental Science, Data Science, Statistics, Computer Science ... Experience with GIS tools and spatial data analysis. * Understanding of data governance and ...

Bachelor's degree in Forestry, Environmental Science, Data Science, Statistics, Computer Science ... Experience with GIS tools and spatial data analysis. * Understanding of data governance and ...

Apply advanced knowledge in economics, statistics, and data science to develop forecasting models ... and spatial analysis techniques to support scenario planning. * Conduct simulations to evaluate ...

Data Analyst

Houston, TX · On-site

$60K - $75K/yr

Apply advanced knowledge in economics, statistics, and data science to develop forecasting models ... and spatial analysis techniques to support scenario planning. * Conduct simulations to evaluate ...

Creating and managing spatial data from multiple sources * Field data collection using GPS * Integrating GPS data into GIS systems * Bachelor's degree in Geology, Earth Science, GIS, or related field

The role involves gathering, creating, converting, and analyzing spatial data to support ongoing ... Science, Engineering, Computer Science, or related field Preferred : • FERC Experience is ...

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Spatial Data Science information

See Texas salary details

$41.5K

$120.9K

$165.4K

How much do spatial data science jobs pay per year?

As of Jul 1, 2026, the average yearly pay for spatial data science in Texas is $120,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,700.00 and $128,100.00 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 GIS jobs pay the most?

Senior GIS analyst, GIS manager, and geospatial data scientist roles tend to offer the highest salaries in the GIS field, often exceeding $80,000 to $100,000 annually depending on experience, location, and industry. These positions typically require advanced skills in GIS software, programming, and data analysis, with certifications like GISP enhancing earning potential.

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.

Can data scientists make $300k?

Data scientists, including those specializing in spatial data science, can earn $300,000 or more at senior levels or in high-demand industries, especially with extensive experience, advanced skills in machine learning, and proficiency in tools like Python or R. Achieving this salary often requires working in large companies, consulting roles, or locations with high living costs, and may involve additional responsibilities or leadership positions.

What does a spatial data scientist do?

A spatial data scientist analyzes geographic data to identify patterns, trends, and relationships 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, or logistics.

Is GIS a high demand job?

GIS (Geographic Information Systems) professionals, including those in spatial data science, are in high demand across industries such as urban planning, environmental management, and transportation. The increasing use of spatial analysis, remote sensing, and GIS tools like ArcGIS and QGIS contributes to strong job growth and opportunities for skilled workers.

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 are popular job titles related to Spatial Data Science jobs in Texas? For Spatial Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Spatial Data Science jobs in Texas look for? The top searched job categories for Spatial Data Science jobs in Texas are:
GIS Analyst

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Posted yesterday


Job description

DSA is seeking qualified GIS Analyst with an active Secret clearance to support operations at NAS Kingsville. This role supports critical Navy geospatial infrastructure and requires strong technical expertise in ESRI platforms and enterprise GIS environments.

Required Qualifications
  • Active Secret clearance
  •  GIS experience
  • Advanced proficiency with:
    • ArcGIS Desktop (10.x)
    • ArcGIS Pro
    • ArcGIS Online
    • ArcGIS Enterprise
  • Experience working in a versioned GIS environment
  • Hands-on experience:
    • Creating and managing spatial data from multiple sources
    • Field data collection using GPS
    • Integrating GPS data into GIS systems
  • Bachelor's degree in Geology, Earth Science, GIS, or related field
  • CompTIA Security+ (or ability to obtain)
Desired Qualifications
  • GIS Professional (GISP) certification
  • ESRI technical certifications
  • Additional experience in geospatial data governance or enterprise systems
Key Responsibilities
  • Maintain and update GIS data within the Navy SE Enterprise Spatial Database Engine (SDE)
  • Coordinate with the Real Property Accountable Officer (RPAO) to reconcile discrepancies and support audit readiness
  • Create, update, and manage standardized geospatial products, including:
    • Symbology layers
    • Map templates
    • Regional and installation-level map products
  • Perform quality control throughout the map production lifecycle
  • Apply cartographic standards and best practices
  • Support stakeholder requests and incorporate user feedback into GIS products

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