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Geospatial Data Science Jobs in California (NOW HIRING)

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Shipt

Data Scientist

San Francisco, CA · On-site +1

Data Science, Geospatial Data, Geospatial Modeling, Python (Programming Language) Bachelor's Degree or equivalent experience| Required Work Arrangement Shipt considers candidates located near a Shipt ...

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All JobsGeospatial Data Science JobsGeospatial Data Science Jobs in California

Geospatial Data Science information

See California salary details

$21.7K

$76.5K

$120.4K

How much do geospatial data science jobs pay per year?

As of Jun 22, 2026, the average yearly pay for geospatial data science in California is $76,480.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,800.00 and $79,000.00 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 California? The most popular types of Geospatial Data Science jobs in California are:
What are popular job titles related to Geospatial Data Science jobs in California? For Geospatial Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Geospatial Data Science jobs in California look for? The top searched job categories for Geospatial Data Science jobs in California are:
What cities in California are hiring for Geospatial Data Science jobs? Cities in California with the most Geospatial Data Science job openings:
Geospatial Data Science jobs near you
Infographic showing various Geospatial Data Science job openings in California as of June 2026, with employment types broken down into 1% As Needed, 88% Full Time, 1% Temporary, 9% Contract, and 1% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $76,480 per year, or $36.8 per hour.
Software Engineer, Geospatial Data (Starshield)

Software Engineer, Geospatial Data (Starshield)

SpaceX

Hawthorne, CA • On-site

$115K - $139K/yr

Full-time

Posted 6 days ago

SpaceX rating

8.7

Company rating: 8.7 out of 10

Based on 144 frontline employees who took The Breakroom Quiz

13th of 60 rated aerospace companies

Job description

Job Summary:
SpaceX is a pioneering aerospace company focused on enabling human life on Mars through advanced technology. They are seeking a Software Engineer for their Geospatial Data team to develop reliable processing systems for earth observation data to support national security efforts. The role involves creating software and machine learning systems for satellite data processing and requires collaboration across various phases of development.
Responsibilities:
• Develop novel geospatial data processing models to extract information from raw and partially processed sensor data
• Create highly reliable software and machine learning systems that task remote sensor payloads on hundreds of satellites in low earth orbit and process the collected information
• See your modeling work and software through from start-to-finish: from figuring out the core needs to prototyping, developing, and testing; to production rollout and beyond
Qualifications:
Required:
• Bachelor's degree in computer science, engineering, math, or scientific discipline; OR 2+ years of professional experience in software development in lieu of a degree
• 1+ years of experience in applied machine learning engineering, full stack development, and data science
• 1+ years of development experience in C, C++, or Python experience
• This position requires successfully obtaining and maintaining a Top Secret Security Clearance as a condition of employment.
• Must be willing to work extended hours and weekends as needed
Preferred:
• Experience applying modern computer vision algorithms at scale
• Experience with software that has been used in real-world applications and projects
• Experience with AWS or building inside of cloud environments
• Experience with developing within Linux server environments, SSH, scripting, and configuration
• Experience with Kubernetes or similar container orchestration frameworks
• Experience with PostgreSQL or implementing highly performant databases
• Ability to obtain and maintain a Top Secret or Top Secret SCI clearance
Company:
SpaceX develops and operates rockets, satellite networks, and AI infrastructure including launch, connectivity, and cloud services. Founded in 2002, the company is headquartered in Hawthorne, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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