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Environmental Data Science Jobs (NOW HIRING)

RELX Group plc

Manager Data Science

Philadelphia, PA · On-site

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps ... or product environments. * Technical expertise across machine learning, generative AI, large ...

RELX

Manager Data Science

Philadelphia, PA · On-site

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps ... or product environments. * Technical expertise across machine learning, generative AI, large ...

Elsevier

Manager Data Science

Philadelphia, PA · On-site

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps ... or product environments. * Technical expertise across machine learning, generative AI, large ...

Fidelity Investments

Director, Data Science

Durham, NC · On-site

Leads and oversees end-to-end data science initiatives, guiding teams through data cleansing ... DE migrating existing AI/ML processes from on-premise environments to AWS platforms, using Extract ...

Valero Energy

Data Science Specialist

San Antonio, TX · On-site

Valero is looking for a passionate and technically accomplished Data Scientist to develop and ... environment built on teamwork, excellence and career growth. To explore the ways we build a ...

Valero

Data Science Specialist

San Antonio, TX

Valero is looking for a passionate and technically accomplished Data Scientist to develop and ... environment built on teamwork, excellence and career growth. To explore the ways we build a ...

Unilever

Data Science Manager

Hoboken, NJ

Proven experience leading data science teams working on complex analytics initiatives in a commercial or product-driven environment. Strong background in data science modelling, with deep expertise ...

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Environmental Data Science information

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$37.5K

$122.7K

$196.5K

How much do environmental data science jobs pay per year?

As of Jun 15, 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 in research or consulting settings.

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 it later in their careers. Gaining relevant knowledge in programming, statistics, and environmental science can be achieved at any age, and employers often prioritize expertise and problem-solving ability over age-related factors.

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:
What job categories do people searching Environmental Data Science jobs look for? The top searched job categories for Environmental Data Science jobs are:
Environmental Data Science jobs near you
Manager Data Science

Manager Data Science

RELX Group plc

Philadelphia, PA • On-site

$115K - $192K/yr

Full-time

Posted 4 days ago

Job description

AI for Science, Research Intelligence & Knowledge Discovery
Lead the Teams Building AI That Advances Science
What if the teams you lead could help accelerate scientific breakthroughs, improve healthcare outcomes, and expand human knowledge?
At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians, educators, and institutions to discover evidence, connect ideas, uncover insights, and solve some of the world's most important challenges.
Every day, millions of researchers depend on our products to navigate an ever-growing universe of scientific knowledge. As a Data Science Leader, your work will directly influence how knowledge is discovered, understood, trusted, and applied across the global research ecosystem.
This is leadership with purpose. This is AI in service of science.
About the team
As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline.
About the Role
As a Data Science Leader, you will build, develop, and inspire high-performing teams responsible for delivering advanced AI, machine learning, search, retrieval, NLP, and generative AI solutions that power scientific discovery and research intelligence.
You will provide strategic direction, elevate technical excellence, and help shape the future of AI-enabled products used by researchers and healthcare professionals worldwide. Working at the intersection of cutting-edge technology and meaningful impact, you will guide teams solving some of the most complex and intellectually challenging problems in science.
Success in this role requires a balance of technical depth, people leadership, strategic thinking, and a passion for helping others do their best work while advancing a mission that matters.
What You'll Do
  • Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
  • Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
  • Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
  • Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
  • Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
  • Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
  • Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
  • Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
  • Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
  • Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.

What We're Looking For
  • Significant experience leading data science, machine learning, artificial intelligence, NLP, information retrieval, or related technical teams.
  • Proven success building, coaching, and developing high-performing teams in complex technology or product environments.
  • Technical expertise across machine learning, generative AI, large language models, retrieval systems, experimentation, and model evaluation.
  • Experience delivering AI-powered products or platforms from concept through production deployment and measurable impact.
  • Deep understanding of modern AI approaches, including LLMs, RAG architectures, semantic search, embeddings, and knowledge systems.
  • Experience establishing evaluation frameworks, experimentation practices, and performance metrics for AI solutions.
  • Ability to translate ambiguous challenges into clear strategy, execution plans, and business outcomes.
  • Exceptional communication and stakeholder-management skills with the ability to influence technical, product, and executive audiences.
  • Experience working with large-scale structured, semi-structured, and unstructured data in production environments.
  • A passion for advancing science, expanding access to knowledge, developing people, and applying AI to create meaningful real-world impact.

Why Join Elsevier
Because your leadership will matter.
You will lead teams building AI systems that help researchers discover knowledge faster, assess evidence more effectively, generate new insights, and accelerate scientific progress.
U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates.If performed in Maryland, the base pay range is $121,200 - $201,900.If performed in New York, the base pay range is $126,900 - $211,500.If performed in New York City, the base pay range is $138,400 - $230,700.If performed in Rochester, NY, the base pay range is $115,400 - $192,300.If performed in New Jersey, the base pay range is $136,213 - $217,587.This job is eligible for an annual incentive bonus.
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