Environmental Scientists and Specialists
Conduct research or perform investigation for the purpose of identifying, abating, or eliminating sources of pollutants or hazards that affect either the environment or public health. Using knowledge of various scientific disciplines, may collect, synthesize, study, report, and recommend action based on data derived from measurements or observations of air, food, soil, water, and other sources.
BLS SOC 19-2041 · O*NET-SOC 19-2041.00 · BLS 2025–2035 · O*NET 31.0
Median annual wage, 2025
$82,220
Projected change, 2025–2035
6.1%
2025–2035 annual average openings
7,300
AI exposure
Very high
Editorial analysis
AI can see environmental patterns at a scale humans cannot watch continuously
Environmental science increasingly draws from satellites, sensors, weather observations, geographic information systems, field sampling, regulatory records, and large historical datasets. AI is particularly useful when those streams become too large for a person to inspect individually.
A wider view, but not necessarily a complete one
Models can help detect land-use change, classify imagery, forecast environmental conditions, identify unusual pollution readings, or reveal patterns across years of observations. But a pattern in the data is only the beginning. Scientists need to know whether a sensor is reliable, whether the sampling method creates bias, and whether a statistical relationship has a plausible environmental explanation.
- Remote-sensing analysis can cover larger areas more quickly.
- Environmental monitoring may identify emerging problems sooner.
- Reports and regulatory documentation can be easier to assemble and search.
- Field investigation, sampling design, regulatory interpretation, community context, and scientific validation remain important.
The environmental scientist of the future may spend less time manually processing observations and more time investigating what the automated systems reveal. People who combine field knowledge with data and geospatial skills may be particularly valuable because they can connect the digital model to the landscape it is supposed to represent.
Labor-market picture
Two measures, different questions
These measures offer two different ways to understand the occupation: BLS projections show how national employment may change over time, while relative AI exposure highlights where tasks may intersect with AI-related capabilities. Read them with the work profile and your local context to decide what deserves closer investigation.
Employment
- Employment, 2025
- 93,400 jobs
- Projected employment, 2035
- 99,200 jobs
- Projected change, 2025–2035
- 5,700 jobs (6.1%)
Relative AI exposure
BLS category: Very high. This is a relative task-exposure classification, not a forecast of employment change, automation, wages, or worker replacement.
Relative AI exposure · Very high
Work profile
Representative tasks
These task statements come from O*NET 31.0. They are source facts, not labels assigned by the AI exposure measure.
- Communicate scientific or technical information to the public, organizations, or internal audiences through oral briefings, written documents, workshops, conferences, training sessions, or public hearings.
- Monitor effects of pollution or land degradation and recommend means of prevention or control.
- Collect, synthesize, analyze, manage, and report environmental data, such as pollution emission measurements, atmospheric monitoring measurements, meteorological or mineralogical information, or soil or water samples.
- Review and implement environmental technical standards, guidelines, policies, and formal regulations that meet all appropriate requirements.
- Provide scientific or technical guidance, support, coordination, or oversight to governmental agencies, environmental programs, industry, or the public.
- Process and review environmental permits, licenses, or related materials.
Skills that matter
Skills in the source profile
Importance uses O*NET's 1–5 scale. Level uses its 0–7 scale. Select a skill to see its source definition and reported values.
Technology and tools
These technology records come from O*NET and show software or tools reported in the occupation profile. They describe work context, not a required checklist for every employer.
- Analytical or scientific software
- Document management software
- Graphics or photo imaging software
- Computer aided design CAD software
- Object or component oriented development software
- Data base user interface and query software
- Compliance software
- Geographic information system
Preparation
- Typical entry education
- Bachelor's degree
- Related experience
- None
- On-the-job training
- None
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Source provenance
Labor-market and exposure facts: bls-ep-2025-35. Work profile: onet-31.0. Verify release details on the Sources page.