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AI Research Scientist

Source-cited RoleMath page about AI Research Scientist.

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Researched by RoleMath Research. Every figure on this page traces to the official source shown next to it.

AI Research Scientist

Quick Verdict

AI Research Scientist is a tech-industry role tracked under the BLS occupation Computer and Information Research Scientists (SOC 15-1221). The figures below are occupation-level; they cover multiple job titles sharing the same BLS measurement group.

Labor Market Context

SOCBLS OccupationMedian Annual WageProjected Change (10-yr)Annual Openings (thousands)Job Zone
15-1221Computer and Information Research Scientists$140,30019.7% (much faster than average)3.25

This role is measured under the BLS occupation Computer and Information Research Scientists (SOC 15-1221).

Guardrail: These are BLS occupation-level statistics, not salary guarantees or outcome predictions for individuals. Wages vary by employer, geography, experience, and specialization.

Related Certifications

CredentialRelationshipExamWhy it matters
—No seeded certs for this role yet——

Sources

  • BLS OEWS (national wage data): https://www.bls.gov/oes/special-requests/oesm25nat.zip
  • BLS Employment Projections (10-year outlook): https://www.bls.gov/emp/ind-occ-matrix/occupation.xlsx
  • O*NET occupational data: https://www.onetcenter.org/database.html

Citation Ledger

IDSupportsEvidenceSource
CIT-01Occupation wage ($140,300)BLS OEWS national, SOC 15-1221BLS OEWS
CIT-02Occupation outlook (19.7%)BLS Employment Projections, SOC 15-1221BLS EP
CIT-03Annual openings (3.2k)BLS Employment Projections, SOC 15-1221BLS EP
CIT-04Job Zone (5)O*NET, SOC 15-1221O*NET

AI & this career

What we can — and can’t — tell you about AI and this role

Cited context only: an occupation-level outlook, descriptive usage data, an employer-language sample, and attributed research — kept separate. No RoleMath AI score, no automation timeline, no job-loss prediction. How we source this →

Occupation outlook · BLS

Where the occupation is projected to go

no BLS Employment Projections row for this role.

How AI shows up in the work

Descriptive usage, not demand or loss

For this shared SOC, the May 2026 usage sample reports 42.07% augmentation-labeled and 57.93% automation-labeled Claude conversations. Anthropic Anthropic Economic Index dataset, CC-BY.

Across all occupations the same dataset splits 51.4% augmentation / 48.6% automation (May 2026) — shown so a single role’s number is never read as an outlier.

Descriptive Claude usage data, not employment demand, not job loss, and not a personal forecast; CC-BY attribution required.

Employer language · sample

What a posting sample mentions

not yet in the posting sample.

Published research · attributed

What independent research says (not RoleMath’s claim)

  • Eloundou et al. estimate that about 80% of U.S. workers have at least 10% of their work tasks exposed to large language model capabilities (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • Eloundou et al. estimate that about 19% of U.S. workers have at least 50% of their work tasks exposed to large language model capabilities (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • Eloundou et al. explicitly disclaim any forecast of AI adoption or timing, describing their measure as capability overlap with tasks rather than a prediction of job loss (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • OECD reports that high-skill occupations are the most exposed to AI on task-overlap measures (OECD Employment Outlook 2023). Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • OECD reports that, as of 2023, there is little empirical evidence of negative employment effects from AI (OECD Employment Outlook 2023). Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • OECD and the AIOE research find that AI exposure and automation risk often run in opposite directions, with the most-exposed high-skill occupations tending to be the least at risk of automation. Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • Felten, Raj and Seamans construct an occupation-level AI Occupational Exposure index by linking AI capabilities to O*NET occupational abilities (Strategic Management Journal). Strategic Management Journal (Wiley) exposure = task overlap, not job loss.
  • Stanford Digital Economy Lab researchers find a roughly 16% relative decline in employment for workers ages 22-25 in the most AI-exposed occupations, based on high-frequency ADP payroll data (Canaries in the Coal Mine, working paper). Stanford Digital Economy Lab correlational usage data, not proof.
  • The ILO notes that AI-exposure indicators measure potential task overlap and cannot by themselves establish job loss (Workers' exposure to AI). International Labour Organization exposure = task overlap, not job loss.
  • The Anthropic Economic Index reports no measured systematic rise in unemployment attributable to AI in its usage data. Anthropic correlational usage data, not proof.

Tier A research stays attributed and separate from BLS outlook and employer-language samples.

Every page here connects to the same cited data spine — these are the closest surfaces to what you just read.

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relevant learning programClaude 101relevant learning programAI Fluency: Framework & Foundationsrelevant learning programAnthropic Academy

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