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Environmental Data Science Jobs (NOW HIRING)

Environmental Data Analyst

Hampton, VA · On-site

$72K - $82K/yr

Bachelor's degree in environmental sciences, computer sciences, data science, mathematics, or equivalent fields related to utilities, energy/water management, and/or engineering. * Minimum of 4-5 ...

Provide guidance in value-added ways for our scientists and engineers to leverage data to make ... Required: Proficiency with environmental relational database management systems, including ...

Provide guidance in value-added ways for our scientists and engineers to leverage data to make ... Required: Proficiency with environmental relational database management systems, including ...

Provide guidance in value-added ways for our scientists and engineers to leverage data to make ... Required: Proficiency with environmental relational database management systems, including ...

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... Strong foundation in Python programming in a cloud environment. * Strong quantitative abilities ...

Support cloud-native and hybrid data environments leveraging AWS, Azure, Kubernetes, and modern ... Strong proficiency in Python and experience with modern data science and engineering frameworks.

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Environmental Data Science information

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$37.5K

$122.7K

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How much do environmental data science jobs pay per year?

As of Jul 21, 2026, the average yearly pay for environmental data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What does an environmental data scientist do?

An environmental data scientist analyzes environmental data to identify patterns, assess environmental risks, and support decision-making. They use statistical tools, programming languages like Python or R, and GIS software to interpret data related to climate, pollution, and natural resources, often working with large datasets and models to inform environmental policies and practices.

Can data scientists make $300k?

Environmental data scientists can potentially earn $300,000 or more at senior levels or in specialized roles, especially with extensive experience, advanced skills in machine learning, and working in high-demand industries or organizations. However, such salaries are typically achieved through seniority, leadership positions, or in regions with higher compensation standards.

What is Environmental Data Science?

Environmental Data Science is an interdisciplinary field that uses statistical, computational, and analytical techniques to collect, analyze, and interpret large sets of data related to the environment. Professionals in this field work on issues like climate change, pollution, biodiversity, and natural resource management by extracting meaningful insights from complex environmental datasets. Their work supports decision-making for policy, conservation, and sustainability initiatives. Environmental data scientists often collaborate with ecologists, geographers, and policymakers to address environmental challenges using data-driven approaches.

Is 40 too late for data science?

Environmental Data Science is a field that values skills and experience over age, and many professionals transition into data science later in their careers. Gaining relevant knowledge in programming, statistics, and environmental data tools can enable a successful entry regardless of age, as continuous learning and practical experience are key factors in the field.

What are some common challenges faced by environmental data scientists when working with real-world datasets?

Environmental data scientists often encounter challenges such as incomplete or inconsistent data, varying data formats, and the need to integrate information from multiple sources like sensors, satellites, and field observations. Addressing missing values, data quality issues, and ensuring proper geospatial alignment can be time-consuming but is essential for producing reliable analyses. Collaboration with domain experts and stakeholders is frequently required to interpret findings and ensure that the results are actionable for environmental policy or management decisions.

What is the difference between Environmental Data Science vs Environmental Data Analyst?

AspectEnvironmental Data ScienceEnvironmental Data Analyst
Required CredentialsTypically requires a degree in data science, environmental science, or related fields; often includes programming and statistical certificationsUsually requires a degree in environmental science, geography, or related fields; may include basic data analysis certifications
Work EnvironmentResearch labs, data centers, environmental agencies, or consulting firmsEnvironmental agencies, research organizations, or consulting firms
Employer & Industry UsageUsed in environmental research, climate modeling, and policy analysisUsed in environmental monitoring, reporting, and data interpretation

Environmental Data Science focuses on developing models and algorithms to analyze complex environmental data, often requiring advanced programming skills. In contrast, Environmental Data Analysts primarily interpret and visualize environmental data to support decision-making. Both roles are vital but differ in technical depth and scope.

What is the highest paying environmental science job?

Environmental Data Science roles such as senior environmental data scientists or environmental analytics managers tend to have the highest salaries in the field, often exceeding $100,000 annually. These positions typically require advanced skills in data analysis, programming, and environmental modeling, and may involve leadership responsibilities or specialized expertise in areas like climate modeling or sustainability analytics.

What are the key skills and qualifications needed to thrive as an Environmental Data Scientist, and why are they important?

To thrive as an Environmental Data Scientist, you need strong quantitative skills, expertise in environmental science, and a relevant degree in data science, statistics, or a related field. Familiarity with data analysis tools such as Python, R, GIS software, and experience with large datasets or machine learning techniques is typical. Exceptional problem-solving abilities, communication skills, and attention to detail set top performers apart in this field. These competencies are crucial for effectively interpreting complex environmental data, informing policy, and driving impactful sustainability initiatives.
More about Environmental Data Science jobs
What cities are hiring for Environmental Data Science jobs? Cities with the most Environmental Data Science job openings:
What are the most commonly searched types of Environmental Data Science jobs? The most popular types of Environmental Data Science jobs are:
What states have the most Environmental Data Science jobs? States with the most job openings for Environmental Data Science jobs include:
Infographic showing various Environmental Data Science job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Environmental Data Analyst

Environmental Data Analyst

GSI Environmental Inc.

Olympia, WA • On-site, Remote

$76K - $110K/yr

Full-time

Posted 13 days ago


Job description

Description

GSI is seeking an Environmental Data Analyst who combines technical ability, analytical creativity, and business acumen to strengthen our data management, analysis, and visualization team. The ideal candidate is comfortable across the full data lifecycle—building and maintaining relational databases, developing analysis and automation pipelines in SQL, Python, and/or R, and providing query, reporting, and visualization support in a collaborative organization working closely with technical staff. Familiarity with environmental chemistry data generated from field investigations of water, soil, sediment, air, and biological matrices is valued. The candidate should know how to analyze system requirements and be familiar with relational databases, statistical analysis, and modern data visualization techniques using relevant and current tools.

GSI provides high-quality technical and regulatory expertise to industry and government to address a broad range of environmental issues. We specialize in solving complex environmental problems by leveraging our extensive knowledge of environmental regulations, stressing accurate data collection and analysis, and applying new technologies to our clients' most difficult challenges.

Responsibilities

  • Support environmental investigation projects by maintaining relational databases of environmental chemistry data from historical reports, laboratory test results, web sources, and third-party databases.
  • Build and maintain ETL (extract, transform, load) and analysis workflows in SQL, Python, and/or R to clean, transform, validate, and analyze environmental datasets.
  • Work with multi-disciplinary teams of engineers and scientists to support statistical and spatial analyses.
  • Build dashboards and visualizations, and format tables, charts, and graphs to style guidelines for technical reports and presentations.
  • Organize data in structures that support automation and downstream analysis.
  • Perform quality assurance on analytical databases to ensure data integrity, and identify and correct data gaps, errors, and relational table issues.
  • Support a wide variety of projects ranging from litigation support and site investigation to data mining for research and development.
  • Effectively communicate findings to internal and external clients.
  • Complete assigned tasks on schedule and within budget while meeting quality standards on deliverables.
  • BS/BA or higher degree in data science, computer science, statistics, environmental science, or a related field.
  • 2-5 years of work experience in data management or analysis, with strong relational database skills using PostgreSQL, Microsoft SQL Server, Microsoft Access, or other database management systems.
  • Comfortable writing advanced SQL for data management (beyond basic create, read, update, delete), to clean, transform, and structure data in relational databases.
  • Familiarity with relational database design, data modeling, and normalization.
  • Awareness of data compilation, migration, and backup/retention considerations.
  • Shell scripting (Bash) and comfort working in Linux and command-line environments.
  • Experience using Python and/or R (Tidyverse, Shiny) for data analysis, visualization, dashboards, and web applications, including hands-on project work.
  • Self-motivated and eager to learn, with a drive to adopt new data and analysis techniques, AI, and emerging technologies to improve workflows.
  • Excellent written and verbal communication, and the ability to manage multiple tasks and meet deadlines with limited supervision.
  • Strong problem-solving skills and attention to detail.
  • Additional preferred skillsets and qualifications include:
    • Familiarity with chemistry data, laboratory information management systems (LIMS), data validation, and extraction of data from online sources.
    • Version control (Git) and reproducible, scripted analysis workflows.
    • Application development and deployment, including web frameworks for data tools (e.g., Django, Flask/FastAPI, Dash, Streamlit, R Shiny). Familiarity with containerization (Docker) is a plus.
    • Experience with embedded and analytical database engines (e.g., SQLite, DuckDB) for local analysis, prototyping, and data interchange.

Job Perks

  • Competitive salary and benefits
  • Quarterly and year-end bonuses
  • Flexible work environment with potential for remote work
  • On-the-job training, mentorship, and professional development
  • Participation in conferences, technical presentations, and papers
  • Collaborative atmosphere
  • Base salary will vary depending on qualifications and experience.

The anticipated salary range for this position is $76,000–$110,000. This range represents GSI's good-faith estimate of the base salary for the position and is provided in accordance with applicable pay transparency laws. Starting salary will be determined based on several job-related factors, including relevant experience, education, professional licensure, certifications, technical expertise, geographic location, internal equity, and overall qualifications.