Materials scientists
SOC Code: 19-2032
Materials scientists carries a 32% AI exposure score (Medium automation risk), with a median annual wage of $104,160 and +4.9% projected employment growth from 2024 to 2034 (BLS), affecting approximately 8,700 workers. Full task breakdown, skills, and employer data are below.
Proportion of tasks susceptible to AI automation (O*NET analysis)
At 32% AI exposure, Materials scientists sits 5 points below the 37.2% average across 832 U.S. occupations - more exposed than 37% of them. Most of its core tasks still require human judgment.
Career outlook score
77/100
Materials scientists, weighted across 3 of 3 tracked dimensions
A weighted composite of median wage, projected growth, and AI-automation security (inverted exposure), each benchmarked against every other tracked occupation. It describes this occupation's standing across those three dimensions, not a guarantee of future outcomes.
- Median wage A+ $104,160
Annual median wage, percentile rank vs all tracked occupations
- Projected growth B+ +4.9%
Projected employment change, 2024-2034, BLS Employment Projections
- AI exposure (inverted) C+ 32% exposure
Lower O*NET task-automability scores lower risk; this dimension scores security, not exposure
Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).
Automation-risk inventory
Exclusive cut of 832 occupations by PlainWorkforce automation-risk band. Orthogonal to the continuous AI-exposure distribution chart below and to this occupation's single risk badge, this is the corpus band mix, with Materials scientists in the Medium band.
Credential × growth standing
Bachelor's band with mid-pack BLS pace
Materials scientists shares the Bachelor's gate with 178 peer occupations and lands in Average (378 of 832). Neither the rarest nor the most crowded growth shelf - use wage and AI desks on this page for the differentiator.
Where Materials scientists sits among all occupations
AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.
Where Materials scientists sits by AI exposure
AI-exposure score distribution across U.S. occupations (O*NET task analysis)
32% Lower than most lower than 63% of 832 occupations
Occupations, banded by AI-exposure score
Each bar is a band; taller bars hold more occupations. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.
Source O*NET task data (U.S. Department of Labor) · BLS 2024–2034
Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.
Materials scientists vs. its occupational neighbors
AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Materials scientists is marked in rose; the top-left corner is the most future-proof.
Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).
Employment Projections
Where This Score Comes From
Materials scientists's Medium automation-risk tier is computed from O*NET Database 30.0 task-level analysis, where each documented task the occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.
The employment, wage, and education figures above come from a second, separate federal source: BLS Employment Projections 2024–2034, matched to the O*NET task data by Standard Occupational Classification (SOC 19-2032) code, with the wage figure cross-validated against the BLS Occupational Employment and Wage Statistics (OEWS) May 2024 survey. BLS figures are presented exactly as published, with no adjustment on our part; the SOC-code match is what lets Materials scientists be compared directly against every other tracked occupation on the same basis, not just roles that happen to share a job title.
For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Materials scientists. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure.
Education & Entry Requirements
Top Tasks (O*NET)
- 1. Conduct research on the structures and properties of materials, such as metals, alloys, polymers, and ceramics, to obtain information that could be used to develop new products or enhance existing ones.
- 2. Test metals to determine conformance to specifications of mechanical strength, strength-weight ratio, ductility, magnetic and electrical properties, and resistance to abrasion, corrosion, heat, and cold.
- 3. Test material samples for tolerance under tension, compression, and shear to determine the cause of metal failures.
- 4. Determine ways to strengthen or combine materials or develop new materials with new or specific properties for use in a variety of products and applications.
- 5. Prepare reports, manuscripts, proposals, and technical manuals for use by other scientists and requestors, such as sponsors and customers.
- 6. Plan laboratory experiments to confirm feasibility of processes and techniques used in the production of materials with special characteristics.
- 7. Recommend materials for reliable performance in various environments.
- 8. Supervise and monitor production processes to ensure efficient use of equipment, timely changes to specifications, and project completion within time frame and budget.
- 9. Research methods of processing, forming, and firing materials to develop such products as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.
- 10. Perform experiments and computer modeling to study the nature, structure, and physical and chemical properties of metals and their alloys, and their responses to applied forces.
Key Skills Required
- Reading Comprehension
- Active Listening
- Science
- Critical Thinking
- Complex Problem Solving
- Writing
- Speaking
- Active Learning
- Judgment and Decision Making
- Mathematics
Knowledge Areas
- Engineering and Technology
- Chemistry
- Physics
- Mathematics
- Computers and Electronics
- Production and Processing
- Design
- Mechanical
- English Language
- Education and Training
Frequently Asked Questions
Will AI replace Materials scientists?
Materials scientists has an AI exposure score of 32%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.
What is the job outlook for Materials scientists?
According to BLS Employment Projections 2024-2034, Materials scientists is projected to grow by 4.9% over the decade. Current employment stands at approximately 8,700 workers.
What skills are needed for Materials scientists?
Key skills for Materials scientists include Reading Comprehension, Active Listening, Science, and others. Typical entry-level education is Bachelor's degree.
How much do Materials scientists earn?
The median annual wage for Materials scientists is $104,160, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.
What education is required for Materials scientists?
The typical entry-level education for Materials scientists is Bachelor's degree. Employers generally expect None of related work experience. On-the-job training typically involves None. Requirements can vary by employer and specialization.
Which companies employ Materials scientists?
Materials scientists roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.
AI Exposure Rating
Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.
Nationwide occupations with similar workforce profiles
Two data-derived peer sets for Materials scientists: AI-exposure neighbors and BLS 2034 growth neighbors (distinct comparison neighborhoods, cross-occupation).
Similar AI exposure score
Nearest O*NET task-automability scores nationwide (32% here).
- Architecture teachers, postsecondary · AI exposure 32%
- Area, ethnic, and cultural studies teachers, postsecondary · AI exposure 32%
- Audiovisual equipment installers and repairers · AI exposure 32%
- Bus drivers, school · AI exposure 32%
Similar projected growth (2024–2034)
Nearest BLS employment change rates nationwide (+4.9% here).
- Chemists · growth +4.9%
- Helpers--pipelayers, plumbers, pipefitters, and steamfitters · growth +4.9%
- Pest control workers · growth +4.9%
- Producers and directors · growth +4.9%
Peers are nearest-neighbor matches on published BLS + O*NET metrics among occupations with ≥10,000 workers in 2024; corpus ranks above sort the full tracked set.
Related Occupations
Showing 6 of 30 occupations with a comparable automation-risk profile.
What to do with this
Use Materials scientists' numbers above to compare, not just read in isolation.
- Compare Materials scientists side by side against another occupation on wage, growth, and AI exposure. Workforce comparison tool
- Understand how the AI exposure score above is actually calculated before treating it as a verdict. Reading AI exposure scores
- See the full ranked list of occupations with the lowest automation exposure nationwide. Safest occupations from AI
AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.
Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).