Occupation score change
IMPACT 40research scientist exposure decreased 4.0 points
AI Exposure Score moved from a previous calculation to 49/100 under methodology v1.0. 2 evidence item(s) contributed.
49/100
0.0
Moderate exposure
12 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
2 sources
5 core tasks shown · 1 secondary hidden
Research Scientists Moderate exposure: only 2 of 5 core tasks show high task automation potential : exposure 49/100 (moderate exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)49/100
Methodology 2026-09-a · score v1.0 · input hash 16fa124020d1a9cf
AI capability exposure72/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential49/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption40/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength47/100
Quantity and source quality of the evidence behind the score.
2 evidence item(s) contribute. Calculated 19 hours ago. 12 Oct 2026 UTC.
Overview
Designs and conducts scientific research. Literature review, code and protocol drafting are assisted; experimental validity, ethics and physical bench work are not.
Employment
≈200,000
approx. BLS OES
Median wage
≈$95,000
approx. BLS OES
Tasks tracked
6
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 5/5 · best current capability 86/100
core task · importance 5/5 · best current capability 68/100
core task · importance 4/5 · best current capability 88/100
core task · importance 4/5 · best current capability 66/100
core task · importance 4/5 · best current capability 68/100
Evidence
Evidence trail
2 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- adoptionneutralconfidence 80/100 · magnitude 402 Jul 2024
The OECD Employment Outlook reports that occupations most exposed to AI are predominantly high-skill, and that exposure does not equal job loss, while noting emerging evidence of effects on task composition and entry-level work.
Cross-country official analysis. Used to inform the interpretation that exposure is not a prediction of unemployment.
Source: OECD — Employment Outlook 2024 · Updated 2 Jul 2024
- researchincreasesconfidence 78/100 · magnitude 521 Apr 2021
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
Important because it establishes the *direction* of exposure: much AI exposure sits in high-skill work rather than routine manual work. Included here as a methodology reference and an exposure input.
Source: Felten, Raj & Seamans — Occupational, industry, and geographic exposure to artificial intelligence (Strategic Management Journal) · Updated 1 Apr 2021
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 14 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- Occupation score changeIMPACT 10
research scientist exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 53/100 under methodology v1.0. 2 evidence item(s) contributed.
- AI adoption signal
AI exposure is highest in high-skill occupations, with polarised effects
The OECD Employment Outlook reports that occupations most exposed to AI are predominantly high-skill, and that exposure does not equal job loss, while noting emerging evidence of effects on task composition and entry-level work.
- Research publishedIMPACT 52
AI Occupational Exposure Index built from 10 linked electronic advances
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
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