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Spatial Data Science Jobs in Pennsylvania (NOW HIRING)

Design and implement models to assess and predict environmental exposure across spatial and ... Integrate multi-source spatiotemporal big data and employ machine learning/AI, network analysis ...

... fuel our science-based advancements. Visionaries like you work on teams that save lives by ... Additional responsibilities include building data generation pipelines, troubleshooting complex ...

Data Collection and Analysis: Undertake spatial and non-spatial data analysis. Develop solutions ... The ability to work closely with a varied team of data scientists, data engineers and software ...

Data Collection and Analysis: Undertake spatial and non-spatial data analysis. Develop solutions ... The ability to work closely with a varied team of data scientists, data engineers and software ...

... Science, or Civil Engineering. • Proficiency in database management: create, edit, update, join, append and query data from spatial and non-spatial relational databases including PostgreSQL. • ...

The analyst will take on projects to extract, evaluate, and visualize spatial and non-spatial data ... Science, or Civil Engineering. Master's degree is preferred. Competencies, Knowledge, Skills and ...

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How much do spatial data science jobs pay per year?

As of Jun 11, 2026, the average yearly pay for spatial data science in Pennsylvania is $130,028.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,800.00 and $137,800.00 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 are popular job titles related to Spatial Data Science jobs in Pennsylvania? For Spatial Data Science jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Spatial Data Science jobs in Pennsylvania look for? The top searched job categories for Spatial Data Science jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Spatial Data Science jobs? Cities in Pennsylvania with the most Spatial Data Science job openings:
Senior Data Scientist - Environmental Engineering

Senior Data Scientist - Environmental Engineering

GHD

Allison Park, PA • On-site, Remote

Other

Posted 23 days ago


GHD rating

8.4

Company rating: 8.4 out of 10

Based on 14 frontline employees who took The Breakroom Quiz


Job description

EMPLOYER:                         GHD Services, Inc.

JOB TITLE:                            Senior Data Scientist - Environmental Engineering

LOCATION:                         4068 Mt. Royal Blvd, Gamma Building, Suite 201, Allison Park, PA 15101

OPENINGS:                         1

DUTIES:                

  • Develop, maintain, and optimize data pipelines for large-scale, complex datasets, and design and implement ETL processes to clean, transform, and structure raw environmental data.

  • Design and build scalable and user-friendly applications using Microsoft Power Platform tools (Power BI, Power Apps, Power Automate).

  • Develop custom workflows and integrations to streamline environmental engineering processes and improve operational efficiency.

  • Analyze spatial data using GIS tools such as ArcGIS, QGIS, or Google Earth Engine for environmental and infrastructure projects.

  • Create interactive maps, spatial visualizations, and predictive models for urban planning, resource management, and environmental impact analysis.

  • Integrate geospatial data with machine learning and graph databases for advanced spatial modeling and decision-making.

  • Ensure adherence to environmental compliance standards, including Environmental Protection Agency (EPA) regulations, Environmental, Social, and Governance (ESG) frameworks, and international sustainability guidelines.

  • Conduct risk assessments for regulatory compliance in the oil and gas domain and other high-impact industries.

  • Implement data-driven strategies for monitoring, reporting, and improving environmental metrics, including carbon emissions, water usage, and waste management.

  • Develop tools and analytics to support audits and reporting for ESG initiatives and compliance with industry-specific standards.

  • Design, train, and deploy machine learning models for predictive analytics, anomaly detection, and decision support systems in environmental engineering applications.

  • Employ computer vision (CV) techniques for image analysis, such as remote sensing and urban planning.

  • Utilize natural language processing (NLP) for analyzing textual data such as regulatory documents, reports, and research papers.

  • Design and manage relational and non-relational databases for data storage and retrieval.

  • Implement graph database solutions for spatial data modeling and network analysis in infrastructure planning.

  • Use data-driven insights to support sustainable infrastructure development, environmental impact assessments, and resource optimization, and integrate environmental engineering principles into data science workflows to ensure regulatory compliance and ecological sustainability.

  • Collaborate with cross-functional teams, including civil engineers, environmental scientists, and project managers, to identify opportunities for data-driven solutions.

  • Develop long-term data science strategies aligned with the firm's objectives and client needs.

  • Present technical findings and actionable insights to clients and senior management in a clear and concise manner and translate complex data science concepts into practical solutions for non-technical audiences.

  • Oversee project timelines, budgets, and resources for data science, application development, and geospatial analytics initiatives, and ensure the successful delivery of high-quality analytical solutions and applications on time.

Domestic (U.S.) travel required up to 10%. Telecommuting permitted up to 60%.

REQUIREMENTS:              

Position requires a Master's degree in Data Science, Information Systems, Engineering or related field, and four (4) years of post-baccalaureate experience in job offered or in a Senior Data Scientist - Environmental Engineering - related occupation. 

Position requires experience in the following:

  • Demonstrable proficiency in tools including Apache Spark and/or Hadoop, and cloud platforms such as AWS, Azure, and/or Google Cloud.

  • Expertise in Microsoft Power Platform, including Power BI, Power Apps, and Power Automate, and ability to create low-code/no-code solutions for workflow automation and application design.

  • Demonstrable proficiency in GIS tools such as ArcGIS, QGIS, or Google Earth Engine, understanding of geospatial data processing, spatial analysis techniques, and map creation, and experience integrating geospatial data with advanced machine learning and database technologies.

  • Demonstrable familiarity with regulatory frameworks including EPA standards, ESG reporting guidelines, and environmental impact assessment methodologies, knowledge of compliance requirements for the oil and gas industry, including risk assessments, emissions tracking, and hazardous material monitoring, and proficiency in sustainability metrics, carbon accounting, and environmental auditing tools.

  • Demonstrable experience in Computer Vision and related libraries such as OpenCV.

  • Demonstrable understanding of supervised and unsupervised learning techniques and experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn.

  • Demonstrable expertise in NLP libraries SpaCy, Hugging Face, or NLTK for analyzing regulatory and legal documentation.

  • Demonstrable knowledge of SQL, NoSQL (MongoDB, Cassandra), and graph databases (Neo4j and/or TigerGraph).

  • Solid experience of full-stack web application development with modern frameworks such as React.js, Node.js, Vue, or Next.js.

  • Demonstrable proficiency in Python, R, Java or C++, and experience with data visualization tools Tableau, Power BI, or Matplotlib.

  • Demonstrable analytical and problem-solving skills with a solid understanding of fundamental principles in civil engineering or environmental science.

Domestic (U.S.) travel required up to 10%. Telecommuting permitted up to 60%.

CONTACT:            Apply online. EOE.


 

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About GHD

Sourced by ZipRecruiter

We are committed to solving the world's biggest challenges in the areas of water, energy and urbanisation. GHD is a global professional services company that is a leader in engineering, construction and architectural expertise. Established in 1928, we remain wholly owned by our people. We are 10,000+ diverse and skilled individuals, yet we are proudly 'One GHD' across more than 200 offices, spanning five continents - Asia, Australia, Europe, North and South America, and the Pacific region. Delivering extraordinary social and economic outcomes, we are passionate about building lasting relationships with our partners and clients.

Industry

Engineering professional services

Company size

10,000+ Employees

Headquarters location

Waterloo, ON, CA

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