Data scientists analyze large datasets to uncover patterns and insights that drive decision-making across industries. According to the Bureau of Labor Statistics, computer and information research scientists-the broader occupational category that includes data scientists-earn a median salary of $145,080 annually and work in fields ranging from technology to environmental science. The field is projected to grow 23% from 2023 to 2033, significantly faster than average, with increasing demand for professionals who can apply these skills to environmental challenges.
If you love solving puzzles with numbers and want to make a real environmental impact, data science offers a compelling career path. You'll combine technical skills with scientific knowledge to help organizations understand complex environmental patterns-from tracking climate change to monitoring wildlife populations to predicting pollution levels.
The field has evolved dramatically in recent years. What once required massive computing resources is now accessible to anyone with a laptop and the right training. That's opened up opportunities across environmental science, from small conservation nonprofits to federal agencies like the EPA and NOAA.
While data science as a profession spans all industries, this guide focuses specifically on applying data science skills to environmental challenges-what we might call environmental data science. The core techniques remain the same, but your focus shifts to climate data, ecosystem monitoring, pollution analysis, and conservation metrics rather than e-commerce or financial modeling.
What Does a Data Scientist Do?
Data scientists transform raw information into actionable insights. You'll acquire and process datasets-sometimes collected over years-and turn them into statistical reports, maps, visualizations, and predictive models (mathematical representations that forecast future outcomes based on historical patterns) that help people make better decisions.
In environmental science specifically, you might analyze pollution monitoring data to identify contamination sources, build climate models to project future temperature changes, or create GIS (Geographic Information Systems-software that captures, stores, and analyzes spatial data) visualizations showing habitat changes over time.
Your audience could range from policymakers who need clear recommendations to scientists who want technical depth.
The work happens primarily at a computer, but the impact extends far beyond the office. When you identify patterns in environmental data, you're helping organizations protect ecosystems, reduce pollution, and respond to climate change more effectively.
Modern data scientists work with powerful tools that weren't available even five years ago. You'll use programming languages like Python and R to process data, SQL to query databases, and visualization platforms like Tableau to communicate findings.
Machine learning (algorithms that allow computers to learn from data and make predictions without explicit programming) and artificial intelligence have become essential skills, especially for predictive environmental modeling.
A Day in the Life
Your typical day might start by cleaning and validating new sensor data from air quality monitors across a region. Mid-morning, you'll run statistical analyses to identify pollution trends and anomalies.
After lunch, you're building visualizations for a presentation to city planners about emission patterns. Late afternoon brings a team meeting where you explain your methodology to environmental planners who'll use your insights for policy decisions.
The role requires both technical precision and communication skills. You'll spend part of your time writing code and crunching numbers, and the rest explaining what those numbers mean to people who aren't data experts.
Where Does a Data Scientist Work?
Data scientists work across virtually every sector of the economy. According to recent industry analysis, the largest employer remains finance and insurance at roughly 25%, where professionals analyze market trends and risk patterns.
Environmental applications have grown as organizations recognize the value of data-driven decision-making for sustainability, though specific employment percentages for environmental data science aren't separately tracked by the BLS.
Professional, scientific, and technical services employ about 23% of data scientists, often on a consultancy basis. These professionals might work on environmental impact assessments, climate risk analysis, or sustainability reporting for clients ranging from corporations to government agencies.
Manufacturing represents approximately 11% of positions, where data scientists optimize supply chains and resource use. Business management consulting accounts for 9%, and the federal government employs a significant number, particularly in agencies focused on environmental protection and monitoring.
Environmental Sector Opportunities
If you're specifically interested in environmental work, you'll find growing opportunities at:
- Federal agencies: The EPA uses data scientists to track pollution patterns and evaluate cleanup efforts. NASA employs them for climate and Earth observation missions. NOAA needs data scientists for weather forecasting and ocean monitoring.
- Research institutions: Universities and labs hire data scientists to analyze everything from forest health to water quality to species populations.
- Environmental consultancies: Private firms need data scientists to conduct impact assessments, model environmental changes, and help clients meet sustainability goals.
- Conservation organizations: Nonprofits increasingly rely on data science to track conservation outcomes and optimize resource allocation.
- Green tech companies: Renewable energy firms, environmental monitoring startups, and sustainability platforms all need data expertise.
Remote work has become increasingly common in data science, especially since 2020. Many positions offer flexibility to work from home part-time or full-time, which has opened up opportunities regardless of your geographic location.
What Is the Average Data Scientist Salary?
As of May 2024, data scientists-who fall under the Bureau of Labor Statistics category of computer and information research scientists-earned a median annual salary of $145,080. This represents strong earning potential, especially compared to many other environmental science careers.
The salary range is broad, reflecting differences in experience, location, and industry. The lowest 10% of earners reported salaries around $81,540, while the highest 10% earned $208,620 or more.
Entry-level positions typically start in the $80,000-$90,000 range, with significant growth potential as you develop expertise.
| Percentile | Annual Salary |
|---|---|
| 10th Percentile | $81,540 |
| 25th Percentile | $108,490 |
| Median (50th) | $145,080 |
| 75th Percentile | $183,370 |
| 90th Percentile | $208,620 |
Industry makes a significant difference in compensation. Software publishers offered the highest median salary at $172,430 during this period. Environmental sector positions may pay slightly less than tech industry roles, but they offer the opportunity to apply your skills toward meaningful environmental impact.
Location also affects salary significantly. Data scientists in major tech hubs like San Francisco, Seattle, and Boston typically earn more than the national median, though the cost of living is correspondingly higher.
Data Science Jobs & Job Description
The specific responsibilities you'll take on depend on your experience level and the organization you join. Here's what you can expect at different career stages.
Entry-Level Data Scientist
When you're starting out, you'll generally need a degree in computer science, environmental science with strong quantitative skills, mathematics, or statistics, plus familiarity with at least one analytical software tool or programming language.
Your responsibilities might include:
- Exploratory data analysis: Using descriptive statistics and visualization to understand datasets and identify patterns worth investigating further.
- Data quality work: Collaborating with data engineers to assess data quality, clean datasets, and ensure accuracy before analysis.
- Defined analytics projects: Developing analyses within well-scoped projects to address specific organizational needs or answer research questions.
- Algorithm translation: Working with software developers and engineers to turn analytical methods into functional products or automated systems.
- Reporting and visualization: Creating statistical reports, maps, graphs, charts, and written summaries based on your analysis, then presenting findings to stakeholders.
Senior Data Scientist
As you gain experience, you'll take on leadership responsibilities in addition to hands-on analytical work.
A senior data scientist's role typically includes:
- Project leadership: Serving as the lead data scientist for major initiatives, making methodological decisions, and guiding technical direction.
- Complex analysis: Working with large, diverse, and messy datasets, then selecting and applying appropriate analytical approaches to extract meaningful insights.
- Data strategy: Identifying what data sources are needed for projects and implementing methods to acquire and utilize that information effectively.
- Communication leadership: Translating technical findings into clear insights that diverse audiences can understand and act upon.
- Professional development: Staying current with the latest analytical methodologies, tools, and techniques as the field evolves rapidly.
- Mentorship: Supervising and developing junior data scientists, reviewing their work, and helping them grow their skills.
What Is the Job Demand for Data Scientists?
Data science continues to be one of the fastest-growing career fields globally. The Bureau of Labor Statistics projects employment for computer and information research scientists will grow 23% between 2023 and 2033-much faster than the average for all occupations.
This explosive growth reflects the increasing availability of data and organizations' growing recognition that data-driven decision-making provides competitive advantages. What once required expensive infrastructure is now accessible through cloud computing and open-source tools, democratizing data science across sectors.
In environmental fields specifically, demand appears to be accelerating as climate change creates urgent needs for better data analysis. Organizations need professionals who can process satellite imagery to monitor deforestation, analyze sensor networks tracking air and water quality, model climate scenarios, and measure the effectiveness of conservation interventions.
The sustainability movement has also created demand. Companies face increasing pressure to measure and report environmental impact, creating jobs for data scientists who can develop metrics, track progress, and identify opportunities for improvement.
Industry reports suggest some regions and specializations already face skills shortages. If you can combine data science expertise with environmental science knowledge, you'll likely find yourself well-positioned in the job market.
What Are the Education Requirements to Become a Data Scientist?
The educational pathway to data science offers flexibility, but you'll need strong quantitative skills regardless of which route you take.
High School Preparation
Focus on building a solid foundation in mathematics and computer science. Take calculus, statistics, and any available programming courses. If your school offers AP Computer Science or data analysis electives, those provide valuable preparation.
Bachelor's Degree
You have several good options for undergraduate study. Growing numbers of schools now offer data science or data analytics as specific majors, which provide direct preparation.
If your school doesn't offer these programs, consider mathematics, statistics, computer science, or environmental science with a strong quantitative focus.
Tailor your studies to your career goals through minors and electives. If you want to work in environmental data science, combine your quantitative major with environmental science coursework, or vice versa-major in environmental science while taking extensive statistics, programming, and data analysis classes.
GIS (Geographic Information Systems) coursework is particularly valuable for environmental applications, as is familiarity with geospatial analysis.
These skills are essential if you want to work in fields like climate science, environmental planning, or emergency management.
Master's Degree
Most data science positions expect or require a master's degree. The good news is that many universities now offer data science master's programs, some available entirely online, specifically designed to meet growing demand.
These programs teach transferable skills you can apply across industries. However, if you've tailored your undergraduate studies toward environmental science, look for graduate programs that allow you to continue that specialization.
Some schools offer data science programs with environmental tracks, or environmental science programs with strong data analytics components.
Alternative pathways have also emerged. Data analytics certificates and master's degrees provide focused training, often in shorter timeframes. Some professionals enter the field through intensive bootcamps, though these work best when you already have a quantitative foundation.
Doctoral Studies
A PhD isn't required for most data science positions. You'll want to pursue doctoral studies if you're interested in high-level research positions, developing new analytical methodologies, or working in academia. Some advanced government policy roles also prefer or require a doctorate.
| Degree Level | Typical Duration | Career Entry Point |
|---|---|---|
| Bachelor's | 4 years | Junior analyst, data analyst positions |
| Master's | 1-2 years | Data scientist, senior analyst positions |
| PhD | 4-6 years | Research scientist, methodology development, academia |
| Certificate/Bootcamp | 3-9 months | Career transition (with related background) |
Data Science - Related Degrees
Essential Skills & Certifications
Beyond formal degrees, you'll want to develop specific technical skills and consider relevant certifications:
- Programming languages: Python and R are essential for most data science work. SQL is crucial for database queries. Familiarity with languages like Julia or Scala can be valuable for specific applications.
- Machine learning: Understanding algorithms, model training, and validation is increasingly expected.
- Visualization: Tools like Tableau, Power BI, or programming libraries (matplotlib, ggplot2) for creating compelling visualizations.
- Big data tools: Hadoop, Spark, and cloud computing platforms (AWS, Google Cloud, Azure) for working with large datasets.
- Domain knowledge: For environmental work, understanding ecology, climate science, or environmental policy makes you much more effective.
Professional certifications can strengthen your credentials. Consider certifications in data science platforms (Microsoft Certified: Azure Data Scientist Associate), analytics tools (Google Data Analytics Certificate), or specific methodologies.
For environmental applications, GIS certifications from ESRI can be particularly valuable.
What Kind Of Societies and Professional Organizations Do Data Scientists Have?
Joining professional organizations helps you stay current with methodologies, network with peers, and advance your career. Here are some important organizations for data scientists:
- Data Science Association: This nonprofit provides education, networking opportunities, and certification in data science. They work to improve methods, tools, and applications in this rapidly evolving field through conferences, publications, and professional development resources.
- Data Mining Group: As a vendor-led organization, DMG establishes standards and best practices within the data mining industry. They develop predictive model markup language (PMML) and other tools that help ensure quality and interoperability across the profession.
- Royal Statistical Society: Based in the UK but serving members globally, the RSS is one of the world's largest professional organizations for statisticians and data scientists. They provide publications, conferences, and professional development while promoting the use of statistics for public benefit-particularly relevant if you're interested in environmental policy applications.
Frequently Asked Questions
What does an environmental data scientist do?
Environmental data scientists analyze datasets related to climate, ecosystems, pollution, and natural resources to help organizations make informed decisions. You might process satellite imagery to monitor deforestation, analyze air quality sensor data to identify pollution sources, build models predicting climate impacts, or evaluate the effectiveness of conservation programs. The work combines programming skills, statistical analysis, and environmental science knowledge to turn raw data into actionable insights.
How much do data scientists earn in environmental fields?
Data scientists earn a median salary of $145,080 annually according to May 2024 BLS data for computer and information research scientists. Environmental sector positions may pay slightly less than tech industry roles but still offer strong compensation, typically ranging from $80,000 for entry-level positions to over $150,000 for experienced professionals. Federal agencies like EPA and NOAA follow government pay scales, while private environmental consultancies and conservation organizations vary based on funding and location.
What degree do I need to become an environmental data scientist?
Most positions require a master's degree in data science, environmental science with a strong quantitative focus, statistics, or a related field. You can take different undergraduate paths-major in environmental science with extensive math and programming coursework, or major in data science, computer science, or mathematics while taking environmental science electives. The key is combining technical data skills (programming, statistics, machine learning) with environmental domain knowledge.
Is data science a growing career in environmental fields?
Yes, significantly. The BLS projects 23% employment growth for computer and information research scientists from 2023 to 2033, much faster than average. Environmental applications appear to be expanding particularly rapidly as organizations need better climate modeling, pollution monitoring, sustainability reporting, and conservation outcome measurement. The combination of increasing data availability, climate urgency, and sustainability commitments is creating strong demand for professionals who can bridge data science and environmental science.
What programming languages do environmental data scientists use?
Python is the most widely used language for environmental data science, offering powerful libraries for data analysis (pandas, NumPy), visualization (matplotlib, seaborn), and machine learning (scikit-learn). R is popular for statistical analysis and has excellent geospatial packages. SQL is essential for working with databases. You'll also benefit from learning GIS software like ArcGIS or QGIS, and tools like Tableau or Power BI for creating visualizations that communicate findings to non-technical audiences.
Key Takeaways
- Strong Earning Potential: Data scientists earn a median salary of $145,080 annually, with the top 10% earning over $208,620, making it one of the highest-paying careers in environmental science.
- Explosive Job Growth: The field is projected to grow 23% from 2023 to 2033, significantly faster than average, with particularly strong demand for professionals who can apply data science to environmental challenges like climate modeling and pollution monitoring.
- Flexible Education Paths: You can enter the field through data science degrees, environmental science with a quantitative focus, or related STEM fields, typically requiring a master's degree but with various undergraduate pathways available.
- Technical Skills Required: Success requires proficiency in programming languages (Python, R, SQL), statistical analysis, machine learning, and visualization tools, combined with domain knowledge in environmental science for specialized applications.
- Diverse Work Environments: Opportunities exist across federal agencies (EPA, NOAA, NASA), research institutions, environmental consultancies, conservation organizations, and green tech companies, with increasing remote work flexibility.
Ready to combine your passion for data with environmental impact? Explore degree programs that will prepare you for a career analyzing climate patterns, tracking ecosystems, and using data to protect our planet.
2024 US Bureau of Labor Statistics salary and job growth figures for computer and information research scientists reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2026.
