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Geospatial Data Science Jobs in Arizona (NOW HIRING)

FHR

A428-Python developer rec 5208

Phoenix, AZ · On-site

$60 - $70/hr

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

Geo-Logic Associates

Staff Hydrologist

Scottsdale, AZ · On-site

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

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All JobsGeospatial Data Science JobsGeospatial Data Science Jobs in Arizona

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 Sep 13, 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.

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 key skills and qualifications needed to thrive as a geospatial data scientist?

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.

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 most commonly searched types of Geospatial Data Science jobs in Arizona?

The most popular types of Geospatial Data Science jobs in Arizona are:

Geospatial Data Science jobs near you
Infographic showing various Geospatial Data Science job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, 2% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $72,216 per year, or $34.7 per hour.

A428-Python developer rec 5208

Phoenix, AZ • On-site

FHR
11 - 50 employees
Learn more about FHRexternal

$60 - $70/hr

Other

Re-posted 22 days ago

Job description

Arizona Department of Health Services (ADHS)

100% on site in Phoenix,AZ

Our direct client has an opening for a Python developer rec 5208

This position is up to 12 months with the option of extension. The client is in Phoenix, AZ.

Rates are $55-$65w2 or $60-70 corp to corp.

Please send us your rate and resume.

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 – Advanced experience writing clean, modular Python code for data processing and automation. Familiarity with best practices including PEP 8, 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 Google’s Geocoding API (including authentication, usage limits, and response parsing)

? Familiarity with API security protocols such as OAuth2, API keys, or JWT

? Understanding of API authentication and security protocols

? 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

Skill

Minimum of 5 years of experience in Python development

Demonstrated experience refactoring or modernizing existing codebases for improved maintainability and performance.

Education

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

Experience working with government or public health organizations is a plus.

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