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Environmental Data Science Jobs in Virginia (NOW HIRING)

Data Science Engineer, Lead

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 ...

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 ...

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 ...

... 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 ...

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

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 7, 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 August 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.

Principal Associate, Data Scientist - Audit Data Science

Capital One

Richmond, VA • On-site

$58K - $58K/yr

Full-time

Posted 28 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

93rd of 170 rated banks


Job description

Principal Associate, Data Scientist - Audit Data Science

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description

Innovation is at the heart of everything we do on the Audit Insights and Innovation team. We're not a traditional Data Science team: we build creative ML solutions across multiple domains, such as LLM based chatbots, GenAI powered applications, AML/Fraud identification, and Customer call transcripts intelligence. Opportunities to learn and build fast allow our team members to develop towards their full potential. We partner closely with product, tech, and design teams to enable faster build-to-market cycles for product features that delight our customers with dynamic and integrated experiences.

You will be the driving force to experiment, innovate, and create next-generation features powered by the latest emerging NLP and Generative AI technologies. If you love a fast-paced, highly rewarding environment, and you love being a builder and communicator, this is the place for you.

In this role, you will:

  • Partner with a cross-functional team of data scientists, data analysts, risk professionals, software engineers, and product managers to manage the risk and uncertainty inherent in statistical models in order to lead Capital One to the best decisions

  • Leverage a broad stack of technologies - Python, Conda, UV, AWS, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data

  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

  • Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

  • Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.

  • Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.

  • An LLM practitioner. You have hands-on experience building with open-source LLM models to create reproducible, production-grade pipelines. You leverage AI-assisted development tools like Claude Code to accelerate prototype development, moving quickly from idea to working solution.

  • Collaboration and Communication. You're capable of effectively articulating data insights and analytics strategies to a diverse audience, including auditors, engineers, product managers and leadership.

  • Statistically-minded. You've built models, validated them, and back tested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.

  • A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.

Basic Qualifications:

  • Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:

    • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics

    • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics

    • A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Preferred Qualifications:

  • Master's Degree in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in "STEM" field (Science, Technology, Engineering, or Mathematics)

  • At least 3 years of experience in Python, Scala, or R

  • At least 3 years of experience with machine learning

  • At least 3 years of experience with SQL

  • At least 1 year of experience working with AWS

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

McLean, VA: $161,800 - $184,600 for Princ Associate, Data Science


Richmond, VA: $147,100 - $167,900 for Princ Associate, Data Science










Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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