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Spatial Data Science Jobs in California (NOW HIRING)

... spatial coordinates, time series, molecular structures, metadata, preprints and published papers ... Coordinate Experimental, Data Science, Data Engineering and AI Research teams to translate ...

... spatial coordinates, time series, molecular structures, metadata, preprints and published papers ... Coordinate Experimental, Data Science, Data Engineering and AI Research teams to translate ...

... images, spatial coordinates, time series, molecular structures, metadata, publication artifacts ... Serve as a technical mentor and leader, raising the bar for data science and ML rigor across the ...

... science at unprecedented scale, and translating these advances into practical tools that empower ... images, spatial coordinates, time series, molecular structures, metadata, publication artifacts ...

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Spatial Data Science information

See California salary details

$43.9K

$128K

$175.2K

How much do spatial data science jobs pay per year?

As of Jun 13, 2026, the average yearly pay for spatial data science in California is $128,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 per year, depending on experience, location, and employer.

What is the highest paying GIS job?

The highest paying GIS jobs are often senior roles such as GIS Director, Geospatial Data Scientist, or GIS Manager, with salaries exceeding $100,000 annually. These positions typically require advanced skills in spatial analysis, programming, and leadership, and may involve working with tools like ArcGIS, Python, or SQL.

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.

Is GIS hard to get a job in?

Getting a job in GIS or spatial data science can be competitive, but having strong skills in GIS software like ArcGIS or QGIS, programming languages such as Python or R, and a solid understanding of spatial analysis can improve employability. Relevant certifications and a portfolio of projects also enhance job prospects in this field.

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.

Is 40 too late for data science?

Age is generally not a barrier to entering a data science or spatial data science career, as skills and experience are more important. Many professionals successfully transition into data science later in life by acquiring relevant skills such as programming, statistics, and data visualization, often through online courses or certifications. Employers value diverse experiences, and continuous learning can help you stay competitive regardless of age.

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.

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 growing use of spatial analysis, remote sensing, and GIS software like ArcGIS and QGIS contributes to strong job prospects and competitive salaries in this field.

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 cities in California are hiring for Spatial Data Science jobs? Cities in California with the most Spatial Data Science job openings:
Infographic showing various Spatial Data Science job openings in California as of June 2026, with employment types broken down into 13% Internship, 49% Full Time, 25% Temporary, and 13% Contract. Highlights an 87% In-person, and 13% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.
Staff Applied Research Scientist (Datagrid)

Staff Applied Research Scientist (Datagrid)

Procore

San Francisco, CA • On-site

Full-time

Posted 24 days ago


Job description

We’re looking for a Staff Applied Research Scientist to join Procore’s AI & Frontier Models organization. In this role, you’ll act as the hands‑on technical leader for applied machine learning systems that extract spatial intelligence from construction drawings, BIM, and project data. The primary goal of this role is to design and deliver reliable, scalable ML systems that reduce design risk, improve constructability, and expand the range of spatial problems Procore teams can solve.

As a Staff Applied Research Scientist, you’ll partner with ML engineers, software engineers, product managers, and construction domain experts to lead day‑to‑day technical execution for spatial intelligence initiatives. Use your expertise in applied machine learning, software architecture, and system design to translate complex, ambiguous problems into high‑quality production systems. This is an opportunity to remain deeply hands‑on while shaping technical direction and raising the engineering bar for the team—join us and help define how spatial intelligence shows up in real construction workflows. Apply today.

This role reports reports into the Manager, Software Engineering, and is based in our San Francisco office, supporting Procore's Datagrid AI Division. Given the collaborative and fast moving nature of this work, we are seeking candidates who are available to work onsite 3 days (Hybrid) per week. This is an immediate opening!

What you’ll do

  • Act as the day‑to‑day technical lead for applied ML projects within the Frontier Models & Spatial Intelligence team.

  • Design, implement, and iterate on machine learning systems that analyze 2D drawings and BIM data to detect clashes, inconsistencies, and constructability risks.

  • Lead hands‑on development of model training, evaluation, and inference pipelines in close collaboration with other engineers.

  • Drive proof‑of‑concept and exploratory work to reduce ambiguity and rapidly validate technical approaches.

  • Ensure the long‑term health, performance, and maintainability of the team’s ML codebases and supporting systems.

  • Set and uphold engineering quality standards through code reviews, mentorship, and technical guidance.

  • Collaborate with partner teams to ensure spatial intelligence systems integrate cleanly into Procore’s broader platform and workflows.

  • Proactively identify technical risks, architectural gaps, or operational concerns and address or escalate them appropriately.

What we’re looking for

  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent practical experience.

  • 8+ years of professional experience building production software systems, including applied machine learning components.

  • Strong experience designing, training, and deploying ML models using Python and modern ML frameworks.

  • Solid foundation in computer science fundamentals, including data structures, algorithms, and system design.

  • Experience working with complex or high‑dimensional data such as images, documents, or structured technical datasets.

  • Demonstrated ability to lead technically through direct contribution, mentorship, and architectural decision‑making.

  • Strong system‑level thinking, with an understanding of reliability, scalability, cost, and operational constraints.

  • Clear communication skills and the ability to explain technical decisions and tradeoffs to cross‑functional stakeholders.

Nice to have experience with technologies such as:

  • ML & Data: PyTorch, TensorFlow, NumPy, Pandas, HuggingFace, self‑supervised or multimodal learning workflows

  • Computer Vision & Spatial Data: OpenCV, document understanding pipelines, geometric or graph‑based representations, 2D/3D spatial reasoning

  • Data & Training Infrastructure: Distributed training, experiment tracking, dataset versioning, large‑scale annotation workflows

  • Backend & Systems: Python‑based services, REST or gRPC APIs, batch and streaming data pipelines

  • Cloud & DevOps: Containerized ML services, Kubernetes, cloud compute and storage (AWS, GCP, or equivalent)

  • Quality & Operations: Model evaluation frameworks, monitoring and alerting, performance and cost optimization in production

Additional Information

Base Pay Range:

227,332.00 - 312,581.50 USD Annual

This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.

For Los Angeles County (unincorporated) Candidates:

Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.