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Geospatial Data Science Jobs in Arizona (NOW HIRING)

Python Developer

Phoenix, AZ ยท On-site

$50 - $68.75/hr

... data validation, and geospatial enrichment. The refactored code should follow Python best practices ... Bachelor's degree in Computer Science, Geographic Information Systems, Software Engineering, or a ...

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Data Analyst

Tempe, AZ ยท On-site

Bachelor's degree in mathematics, computer science, or statistics * Three (3) or more years of ... Experience with multiple database and geospatial applications, including PostgreSQL. * Demonstrated ...

... updating tabular data; working knowledge of geospatial data stored in ArcGIS Enterprise ... Education Bachelor's degree in Computer Science, Geographic Information Systems, Software ...

... scientists, engineers, modelers, geographic information system (GIS) specialists, and field ... You have experience with ArcGIS, or similar software for managing geospatial data. * You are ...

... geospatial sources. The ideal candidates will have a BS degree in GIS or equivalent degree or ... Will perform both geographic and non-geographic data manipulation, management and analysis ...

... geospatial sources. The ideal candidates will have a BS degree in GIS or equivalent degree or ... Will perform both geographic and non-geographic data manipulation, management and analysis ...

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Geospatial Data Science information

See Arizona salary details

$20.5K

$72.2K

$113.7K

How much do geospatial data science jobs pay per year?

As of Jun 30, 2026, the average yearly pay for geospatial data science in Arizona is $72,216.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,800.00 and $74,600.00 per year, depending on experience, location, and employer.

How does a Geospatial Data Scientist typically collaborate with other departments or teams within an organization?

Geospatial Data Scientists often work closely with professionals from diverse departments such as urban planning, environmental science, IT, and business analytics. Collaboration usually involves sharing spatial insights, integrating geospatial data with other datasets, and contributing to interdisciplinary projects that require spatial analysis or mapping. Effective communication is crucial, as you'll translate complex geospatial findings into actionable recommendations for non-technical stakeholders. This cross-functional teamwork not only broadens your understanding of organizational goals but also enhances the impact and visibility of geospatial analyses.

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

AspectGeospatial Data ScienceGIS Analyst
Required CredentialsDegree in Data Science, Geography, or related; often includes programming skillsDegree in Geography, GIS, or related; GIS certifications common
Work EnvironmentData analysis, modeling, programming, often in tech or research settingsMapping, spatial data management, using GIS software in various industries
Employer & Industry UsageTech companies, research institutions, government agencies focusing on spatial data analysisUrban planning, environmental agencies, utilities, and government agencies

While both roles work with spatial data, Geospatial Data Science emphasizes data analysis, modeling, and programming skills to extract insights from geospatial data. GIS Analysts focus more on mapping, data management, and using GIS software for spatial analysis. The roles often overlap but differ mainly in technical focus and application areas.

What are the key skills and qualifications needed to thrive as a Geospatial Data Scientist, and why are they important?

To thrive as a Geospatial Data Scientist, you need a solid background in statistics, spatial analysis, and programming, typically supported by a degree in geography, computer science, or a related field. Proficiency with GIS software (such as ArcGIS or QGIS), spatial databases, and coding languages like Python or R is essential, and certifications in GIS can be advantageous. Strong problem-solving skills, attention to detail, and effective communication help translate complex spatial data into actionable insights for diverse stakeholders. These skills ensure accurate data analysis, innovative solutions, and impactful decision-making in fields reliant on geographic information.

What is geospatial data science?

Geospatial data science is an interdisciplinary field that focuses on analyzing and interpreting data that has a geographic or spatial component. It combines techniques from data science, statistics, and geographic information systems (GIS) to extract insights, identify patterns, and solve problems related to location-based data. Professionals in this field work with mapping, remote sensing, spatial analysis, and visualization tools to support decision-making in areas like urban planning, environmental monitoring, and logistics.
What are the most commonly searched types of Geospatial Data Science jobs in Arizona? The most popular types of Geospatial Data Science jobs in Arizona are:
What are popular job titles related to Geospatial Data Science jobs in Arizona? For Geospatial Data Science jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Geospatial Data Science jobs in Arizona look for? The top searched job categories for Geospatial Data Science jobs in Arizona are:
Infographic showing various Geospatial Data Science job openings in Arizona as of June 2026, with employment types broken down into 5% As Needed, 47% Full Time, and 48% Part Time. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $72,216 per year, or $34.7 per hour.
Python Developer

Python Developer

Software Technology Inc

Phoenix, AZ โ€ข On-site

$50 - $68.75/hr

Other

Posted 19 days ago


Job description

Python Developer for Geocoding System Modernization

The Arizona Department of Health Services (ADHS) is seeking a skilled and experienced Python developer to modernize and refactor the Python codebase that supports an existing full-stack geocoding system. This system includes multiple Python scripts for data processing and automation, a SQL Server database, Esri file geodatabases, and a PHP-based web interface for configuring and running geocoding jobs. It also features a manual geocoding interface built with the ArcGIS JavaScript API. Geocoding workflows leverage a combination of locally installed geocoding software, ArcGIS Server-based geocoding services, and the Google Maps API. This contract position will focus on improving the maintainability, clarity, and structure of the existing Python codebase. Core responsibilities include refactoring scripts that handle geocoding logic, address standardization, data validation, and geospatial enrichment. The refactored code should follow Python best practices (including PEP 8), improve error handling and logging, and allow for more efficient testing, debugging, and future enhancement.

Key Responsibilities
  • Assess the current Python codebase, workflows, and dependencies related to geocoding and spatial data processing.
  • Refactor and modernize existing Python scripts to improve code readability, modularity, and adherence to best practices (e.g., PEP 8, logging, error handling).
  • Maintain and improve integration with multiple geocoding services, including locally installed geocoding software, ArcGIS Server REST-based services, and Google Maps Geocoding API (including quota management and API key security).
  • Transition data processing from flat files (CSVs, Esri file geodatabases) to an ArcGIS Enterprise Geodatabase (SQL Server).
  • Implement robust logging, error handling, and validation throughout the codebase.
  • Optimize and document fallback logic for handling geocoding failures and manual resolution workflows.
  • Produce high-quality technical documentation, including code comments and usage instructions, architecture diagrams, and workflow documentation.
  • Improve the logging and reporting framework for better error tracking and system transparency.
  • Collaborate with internal GIS and IT staff for requirements clarification, testing, and implementation support.
Required Skills & Experience

Core Technical Proficiency

  • Python Proficiency โ€“ Advanced experience writing clean, modular Python code for data processing and automation. Familiarity with best practices including PEP8, logging, and error handling; libraries may include: pandas, os, shutil, logging, arcpy, pyodbc, requests.
  • Experience using ArcPy for spatial joins, geoprocessing, and field calculations.
  • Strong understanding of SQL Server databases, including querying and updating tabular data; working knowledge of geospatial data stored in ArcGIS Enterprise Geodatabases.
  • Experience working with third-party geocoding APIs, especially Googleโ€™s Geocoding API (including authentication, usage limits, and response parsing).
  • Familiarity with API security protocols such as OAuth2, API keys, or JWT.

Software Development Best Practices

  • Adherence to PEP 8 and modular design principles.
  • Use of Git for version control and collaboration.
  • Experience writing clean, maintainable, and well-documented code.
  • Comfortable working independently and delivering clear, reliable deliverables in a contract environment.
Preferred Qualifications
  • Experience modernizing or rewriting legacy Python codebases.
  • Familiarity with geocoding accuracy scoring, fallback strategies, and manual resolution workflows.
  • Prior work with public health, government, or GIS teams is a plus.
Deliverables
  • Refactored Python scripts with improved structure, documentation, and testability.
  • Modularized codebase that supports future enhancements and integration of new geocoding services.
  • Revised error handling and logging mechanisms.
  • Documentation including: code comments and docstrings, developer setup instructions, system architecture diagram or data flow chart.
  • Updated configuration files and batch scripts (if applicable).
  • Knowledge transfer session(s) to internal staff.
Qualifications

Education

  • Bachelorโ€™s degree in Computer Science, Geographic Information Systems, Software Engineering, or a related field.
  • Equivalent professional experience may be substituted for formal education.

Experience

  • Minimum of 5 years of experience in Python development.
  • Demonstrated experience refactoring or modernizing existing codebases for improved maintainability and performance.
  • Experience working with government or public health organizations is a plus.