Analyze / Jobs / Rankings / Materials Scientists Benchmark Job
Materials Scientists Materials Scientists remains mostly human-led, with selective automation opportunities in support work.
Research and study the structures and chemical properties of various natural and synthetic or composite materials, including metals, alloys, rubber, ceramics, semiconductors, polymers, and glass. 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. Includes glass scientists, ceramic scientists, metallurgical scientists, and polymer scientists.
40% exposure • High confidenceToday
Category: Life, Physical, and Social Science • Industry: Regulation and Administration of Transportation Programs (92)
Top 3 drivers Routine process execution : 32% task weight, 54% automation potential.Documentation and reporting : 28% task weight, 44% automation potential.Stakeholder communication : 22% task weight, 30% automation potential.Top 2 blockers Exception handling : Edge cases and accountability ownership.Stakeholder communication : Empathy, negotiation, and situational nuance.Confidence reasons 1,016 benchmark role descriptions are mapped across O*NET + NAICS sources. Coverage spans 23 categories and 1,012 industries. Adjacent role consistency band is ±1 points for this benchmark family.
Task Mix ⓘ Routine process execution (32% weight • 17 contribution) 54%
Mixed automation potential with meaningful human oversight.
Documentation and reporting (28% weight • 12 contribution) 44%
High judgment, trust, or contextual complexity keeps this human-led.
Stakeholder communication (22% weight • 7 contribution) 30%
High judgment, trust, or contextual complexity keeps this human-led.
Exception handling (18% weight • 4 contribution) 20%
High judgment, trust, or contextual complexity keeps this human-led.
Recommended Stack Click a stack item to open setup difficulty, time-to-value, and workflow details.
Research assistant Data analysis suite Workflow automation Replaceable tasks vs human tasks AI-suitable tasks ✓ Routine process execution 54% automation potential • Workflow automation + RPA ✓ Documentation and reporting 44% automation potential • Document AI + reporting automation ✓ Stakeholder communication 30% automation potential • Template assistant + CRM automation Human-needed tasks • Exception handling AI struggles because edge cases and accountability ownership. • Stakeholder communication AI struggles because empathy, negotiation, and situational nuance. • Documentation and reporting AI struggles because liability and audit-quality requirements. Tools coverage meter This stack covers 31% of automatable tasks.
Remaining gaps: exceptions, communication, edge cases.
Automation timeline Today tools now 12 months next wave 36 months agentic workflows
Current mode: Today • Exposure estimate: 40% • tools now
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Benchmarked descriptions used: 1,016 role descriptions mapped across 1,012 industries.
Data references for this role Role profile and task mix
Job title, SOC 19-2032.00, role description, task statements, and job-zone context.
Source: O*NET Database 29.0 (Occupation Data + Job Zones) • O*NET Resource Center / U.S. Department of Labor
Industry and sector mapping
Industry code 926120, sector 92, and category mapping shown on this role.
Source: NAICS 2022 6-Digit Codes • U.S. Census Bureau
Exposure score, confidence, and timeline views
Deterministic benchmark scoring, confidence tiers, stack coverage, and timeline projections derived from role/task inputs.
Source: replaced.fyi methodology • replaced.fyi
Related roles and pivot recommendations
Adjacent-role comparisons and safer/higher-pay pivot suggestions computed from the same benchmark catalog.
Source: replaced.fyi rankings dataset • replaced.fyi
Primary source list Confidence definition: High confidence means broad role coverage, benchmark consistency across adjacent roles, and stable task-level scoring signals.
Methodology • Rankings