1

Environmental Data Science Jobs in Virginia (NOW HIRING)

Lead Data Science Engineer

Mclean, VA · On-site

$99K - $225K/yr

Here, you'll work with a multi-disciplinary team of analysts, data scientists, data engineers, developers, and data consumers in a fast-paced and agile environment. As a Data Science Engineer at Booz ...

Deploy and monitor machine learning models in production environments * Continuously improve model performance and accuracy * Stay current with emerging data science techniques, tools, and ...

Deploy and monitor machine learning models in production environments * Continuously improve model performance and accuracy * Stay current with emerging data science techniques, tools, and ...

... Environment. In this role, you will work autonomously to find creative solutions and tackle ... As a part of the Data Science team you'll have opportunities to work on projects that expand your ...

Deploy and monitor machine learning models in production environments * Continuously improve model performance and accuracy * Stay current with emerging data science techniques, tools, and ...

... Environment. In this role, you will work autonomously to find creative solutions and tackle ... As a part of the Data Science team you'll have opportunities to work on projects that expand your ...

... Environment. In this role, you will work autonomously to find creative solutions and tackle ... As a part of the Data Science team you'll have opportunities to work on projects that expand your ...

... environments. You will work directly with government clients, program managers, and technical teams ... You will contribute across the full data science lifecycle, from problem formulation and data ...

... Data Science, Statistics, Mathematics, Computer Science, or related field • 5-9 years of ... Agile delivery environments • Experience supporting IC, DoD, or DOJ mission programs • ...

Key Responsibilities * Lead development and application of advanced data science, AI/ML ... Defense environment. * Experience developing ROI-based analytical models, COA comparison frameworks ...

Showing results 21-40

Environmental Data Science information

See Virginia salary details

$37.2K

$121.7K

$194.8K

How much do environmental data science jobs pay per year?

As of Aug 6, 2026, the average yearly pay for environmental data science in Virginia is $121,686.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,700.00 and $134,800.00 per year, depending on experience, location, and employer.

Is environmental data science a good major?

Environmental data science is a strong major for those interested in analyzing environmental data, using tools like GIS and statistical software. It prepares students for roles in environmental monitoring, research, and policy, often requiring skills in programming, data analysis, and environmental science. Job prospects are growing as organizations seek data-driven solutions to environmental challenges.

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.

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.

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 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.
What are the most commonly searched types of Environmental Data Science jobs in Virginia? The most popular types of Environmental Data Science jobs in Virginia are:
What cities in Virginia are hiring for Environmental Data Science jobs? Cities in Virginia with the most Environmental Data Science job openings:
Infographic showing various Environmental Data Science job openings in Virginia as of July 2026, with employment types broken down into 96% Full Time, and 4% Part Time. Highlights an 100% In-person job distribution, with an average salary of $121,686 per year, or $58.5 per hour.

Data Science Lead, Personnel Health Research & Data Analytics

Fors Marsh

Alexandria, VA • On-site

$100K - $120K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 2 days ago


Job description

Data Science Lead: Personnel Health Research & Data Analytics, Military (Washington, D.C.)

WHO WE ARE: At Fors Marsh, we take on issues that matter. We are a team of researchers, strategists, and communicators working together to drive lasting change. We look at human behavior from all angles with a deep understanding of people and context to design solutions that influence decision-making and move people to action. Our work promotes health and well-being, shapes resilient communities, and builds effective and accountable institutions. We are a certified B Corporation and a Top Workplace for 7 consecutive years.

WHO WE ARE LOOKING FOR:

Fors Marsh is seeking an intelligent and motivated Researcher for a senior position with a background in quantitative social science or data science. This individual's primary responsibility would be to support a portfolio of quantitative social science and data science research projects for our Personnel Health Research & Data Analysis (PHRDA) team. PHRDA bridges the gap between social science and data science, leveraging innovative analytics tools for research committed to improving the health, wellbeing, and quality of life for Service members and DoW personnel. We routinely synthesize diverse sources of information, including administrative, survey, and text data and distill complex information for policymakers. We also support a wide variety of data science and operational tasks that enable our clients to successfully and efficiently collect, integrate, manage, and maintain a variety of administrative and survey data sources.

This individual's primary responsibility would include providing subject matter expertise and methodological expertise in areas such as machine learning, data wrangling, data management, advanced quantitative analysis, text analysis, automation, and artificial intelligence. This role will also involve overseeing a large project of foundational data science capabilities and will include leading a team of researchers, analysts, and data scientists as well as frequent engagement with clients. This individual should be equal parts social scientist, data scientist, and project manager. This job is best for someone who enjoys solving challenging problems with a large methodological toolkit, a high degree of intellectual curiosity, and thrives in a collaborative environment.

Responsibilities include:

  • Analytical and Technical Skills
    • Apply sophisticated principles in the fields of data science, quantitative social science, and/or programming to social science research projects and data science operational projects
    • Serve as the technical lead on research projects with a data science focus
    • Designing analytic solutions to ambiguous research questions and goals that may include techniques such as multi-level modeling (e.g., cross-classified random effects, linear mixed effects), data reduction (e.g., PCA, factor analysis, clustering), predictive model selection (e.g., LASSO, Ridge), natural language processing, and/or machine learning techniques
    • Interpret results from data analyses to identify patterns, solutions, and recommendations; distill complex analyses and associated results for technical and non-technical audiences
    • Communicate trade-offs associated with analytic approaches to technical and non-technical audiences
    • Oversee survey workflows focused on population specification and sample frame development, including identifying relevant data sources, cleaning, deduplication, and frame quality checks
    • Leverage AI tools to accelerate research, analysis, and reporting; lead the development internal prototypes and proofs of concept; enhance client offerings and expand AI capabilities; train other team members to build further AI proficiency
    • Oversee work in R, Python, and Databricks to execute automation tasks, R package creation, data wrangling, exploratory data analysis, and executing advanced analyses
  • Project Management
    • Oversee large, complex project to include developing task plans and timelines, implementing scope and financial controls, aligning staffing resources to project technical requirements, and ensure rigorous and timely delivery of all deliverables
    • Lead a matrixed team of data scientists, researchers, and analysts to include delegating tasks and overseeing task and project completion, providing support, guidance, and mentorship to junior team members
    • Collaborate with other project leaders and supervisors across the organization to accomplish project resourcing needs
  • Client and Stakeholder Interaction and Communication
    • Develop a deep understanding of client mission and objectives; directly interface with client teams and build strong, trusting relationships with clients to collaboratively accomplish project goals
    • Ensure client satisfaction with all project deliverables
    • Lead the development of client briefings and reports and mentor team members to deliver high-impact briefings to clients
    • Oversee the preparation of reports for technical and non-technical audiences
    • Respond to client requests for information and proposals and ad hoc requests
  • Supervisory Responsibilities and Team Orientation
    • Oversee a small team of data scientists and analysts
    • Work collaboratively with a mixed team of data and social scientists, cultivate a productive, growth-oriented work environment, propel team development

Qualifications:

  • Master's degree required; PhD preferred in a heavily quantitative social science (e.g., sociology, political science, psychology), data science, or related field
  • A minimum of four years of post-graduate applied research experience leading applied research projects
  • Experience managing multiple concurrent work streams, delegating and overseeing tasks, and supervisory experience
  • Experience interfacing directly with clients, overseeing high-quality deliverable creation, and ensuring projects align with client expectations and quality standards
  • Proficiency with AI-enabled tools
  • Demonstrated experience working with diverse data sources, including survey, personnel, administrative, and text data
  • Working knowledge of survey workflows supporting population definition, sample frame construction, and feature table creation to assist in survey package and tool development (R and Python)
  • Willingness and ability to learn new analytic approaches, methods, and research topic areas
  • Strong verbal and written communications skills, including the ability to describe technical concepts and results to both technical and non-technical audiences
  • Strong problem-solving skills and the ability to troubleshoot barriers as they arise
  • Experience working in R/R Studio required
  • Experience with Databricks required
  • Experience with Python required
  • Experience with SQL preferred
  • Experience with Gitlab preferred
  • Applicants will be subject to a government security investigation and must meet eligibility criteria for access to sensitive information.
  • Ability to work on-site in the Washington D.C. area 2-3 days a week.
  • Must be a U.S. Citizen and consent to a full background check due to our federal contract requirements.

We Offer:

Our benefits typically meet or exceed our competitors' packages. What's in it for you?

Ability to make an impact on people's lives, both internal and external to the organization.

Top-tier health and dental covered at 100% for employee coverage.

Additional vision, and long and short-term disability options.

Our company culture, which values balance and allows each employee to take leave as they require it to balance the responsibilities of both their work and home lives without worrying about depleting their available leave hours.

A floating holiday bank so you can celebrate the days you value.

Generous matching retirement contributions and no vesting period starting the third month of employment.

Dedicated training and development budgets to expand your expertise and grow your skillset.

You can volunteer your way with paid time off.

You can participate in Fors Marsh staff-led affinity groups.

Our employees receive product and service discounts through the certified B Corp network.

Salary:$100,000-$120,000 annual

Internal Fors Marsh Career Map Title: Senior Scientist II

Location: Hybrid. Individuals are expected to go into a federal government building in Alexandria, VA, and work on-site 2-3 days a week during business hours. #LI-Hybrid

Equal Opportunity Employer:All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

Employment Type: FULL_TIME