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Spatial Data Science Jobs in Pittsburgh, PA (NOW HIRING)

Varsity Tutors

AP Human Geography Tutor

Pittsburgh, PA · Remote

$40/hr

Skilled at teaching geographic model application, spatial data analysis, and free-response question ... science to create personalized learning experiences. Through 1-on-1 Online Tutoring, students ...

AEG

Research Engineer (AI + Sports)

Pittsburgh, PA · On-site

... game data at scale across multiple sports, teams, and seasons * Create spatial and temporal ... PhD in Computer Vision, Machine Learning, Computer Science, or a closely related field * Strong ...

All JobsSpatial Data Science JobsSpatial Data Science Jobs in Pittsburgh, PA

Spatial Data Science information

See Pittsburgh, PA salary details

$43.2K

$125.9K

$172.3K

How much do spatial data science jobs pay per year?

As of Sep 11, 2026, the average yearly pay for spatial data science in Pittsburgh, PA is $125,927.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,200.00 and $133,500.00 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 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 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.

What does a spatial data scientist do?

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

What are popular job titles related to Spatial Data Science jobs in Pittsburgh, PA?

For Spatial Data Science jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Spatial Data Science jobs in Pittsburgh, PA look for?

The top searched job categories for Spatial Data Science jobs in Pittsburgh, PA are:

What cities near Pittsburgh, PA are hiring for Spatial Data Science jobs?

Cities near Pittsburgh, PA with the most Spatial Data Science job openings:

Spatial Data Science jobs near you
Infographic showing various Spatial Data Science job openings in Pittsburgh, PA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 67% Full Time, 29% Part Time, and 2% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $125,927 per year, or $60.5 per hour.

Computational Data Scientist II

Pittsburgh, PA • On-site

University of Pittsburgh
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about University of Pittsburghexternal

Other

Re-posted 22 days ago

Job description

The Bioinformatics Core within the Division of Health Informatics at UPMC Children's Hospital of Pittsburgh is seeking a computational biologist or data scientist to develop and apply machine learning and deep learning methods for omics-driven biomedical research. This individual will work closely with faculty and collaborators across multiple pediatric research programs to support biologically grounded, translationally relevant research. Equivalent relevant work experience may be substituted for degree requirement. This position is located at UPMC Children's Hospital of Pittsburgh in Lawrenceville. PA Child Abuse History Clearance, PA State Police Criminal Record Check, and FBI Criminal Record Check will be required prior to the start of employment. Also, a current TB test will be required as a condition of employment. EEO/AA/M/F/Vets/Disabled.
Minimum Qualifications
Applicants should have an MS or PhD in computational biology, bioinformatics, computer science, statistics, data science, biomedical informatics, or a related quantitative field.
The successful candidate should have strong programming skills in Python and/or R, with experience using libraries such as PyTorch, TensorFlow, scikit-learn, or comparable tools. Experience in applying computational or statistical modeling to biomedical or biological datasets is expected.
Prior experience working with single-cell, spatial omics, or related high-dimensional omics datasets is highly desirable. Candidates should be strong critical thinkers who can translate ideas into completed analyses, models, or tools; manage contributions across multiple collaborative research projects; work independently and as part of multidisciplinary teams; and demonstrate strong oral and written communication skills.
Preferred qualifications include:
Demonstrated experience designing and implementing models for high-dimensional biological data, beyond routine application of existing analysis pipelines.
Hands-on experience with graph neural networks or perturbation modeling.
Experience integrating omics data with clinical data sources such as electronic health records (EHR).
Evidence of independent technical contribution, such as first-author or co-author publications, preprints, conference presentations, open-source software, deployed tools, analytical pipelines, or a relevant project portfolio.

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