role

AI Research Scientist

Review AI Research Scientist role tasks, pay context, AI-impact notes, skill gaps, and cited evidence.

See my realistic path to AI Research Scientist

Growing occupation - projected +19.7% through 2034.

The call: No certification is currently mapped to this role in RoleMath's cited data. Decide with the labor-market context below, and use the planner to compare adjacent roles that do have mapped credential paths. BLS lists Master's degree as the typical entry education.

BLS projects Computer and information research scientists - the occupation this role maps to - to grow 19.7% from 2024 to 2034. O*NET profiles the work as primarily Investigative (analytical, problem-digging) and Conventional (structured, detail-heavy). Weigh that against how you actually like to work.

What the numbers say about this work

Government occupation data for the role this maps to — Computer and Information Research Scientists (SOC 15-1221). This is planning context for the occupation, not a salary or a job this role guarantees you.

Median pay (occupation): $140,300 / yr · $82,200 to $230,630 (10th–90th percentile)
Projected change (2024–34): +19.7% · ~3.2k openings/yr
Typical entry education: Master's degree

BLS OEWS — occupation-level, national · checked 2026-06-25 BLS Employment Projections 2024–34 · checked 2026-06-25 This role uses a broad O*NET-SOC/BLS occupation mapping. Treat salary, outlook, and task data as occupation-level evidence, not a guarantee for this exact job title.

What it pays by metro

The national median hides a wide geographic spread. Below is the occupation’s median in some of the highest-paying and largest-employment metros, adjusted for local prices — regional price-level context, not take-home pay or a salary this role guarantees you.

Metro	Nominal median	Cost-adjusted
San Jose, CA	$218,420	$197,803
Seattle, WA	$211,270	$190,106
Boston, MA	$170,510	$157,492
New York, NY	$170,890	$151,817
Baltimore, MD	$156,750	$150,019
San Francisco, CA	$171,440	$148,288

See all metros and how this is calculated → Sources: BLS OEWS (May 2025), occupation-level metro median ÷ BEA Regional Price Parities (2024, US=100).

What this work involves

The tasks the U.S. Department of Labor’s O*NET lists most central to this occupation — role-fit evidence to weigh against your background, not a measure of employer demand.

Analyze problems to develop solutions involving computer hardware and software.
Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.
Assign or schedule tasks to meet work priorities and goals.
Meet with managers, vendors, and others to solicit cooperation and resolve problems.
Design computers and the software that runs them.
Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.

O*NET — occupation-level · checked 2026-07-14

Skills that matter

The skills O*NET rates most important for this occupation. A starting map for what to build — weigh it against the specific job you’re targeting.

Critical Thinking
Active Listening
Reading Comprehension
Active Learning
Speaking
Mathematics
Writing
Monitoring

O*NET — occupation-level · checked 2026-07-14

Quick Verdict

AI Research Scientist maps to BLS/OEWS occupation context Computer and Information Research Scientists (SOC 15-1221), which has a national median of $140,300. Pay is occupation-level and location-driven - not caused by the job title or a certification. Below are the full cited labor-market context, the skills the role draws on, and the certification paths that map to it. This role uses a broad O*NET-SOC/BLS occupation mapping. Treat salary, outlook, and task data as occupation-level evidence, not a guarantee for this exact job title.

Fit Signals

Investigative (7)
Conventional (4.8)
Realistic (3.74)

Skills & Tools

*Tools and technologies ONET associates with this occupation* - role-specific examples with ONET hot/in-demand flags, not employer requirements:

Amazon Web Services AWS software (hot technology, in demand)
Ansible software (hot technology, in demand)
Apache Hadoop (hot technology, in demand)
Apache Kafka (hot technology, in demand)
Apache Spark (hot technology, in demand)
Bash (hot technology, in demand)
C (hot technology, in demand)
C++ (hot technology, in demand)

*Foundational ONET skills** (broadly shared across occupations, not unique to this role): Critical Thinking, Active Listening, Reading Comprehension, Active Learning, Speaking, Mathematics.

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 O*NET detail, the May 2026 usage sample reports 42.07% of Claude conversations augmenting the person's work and 57.93% automating a task. Anthropic · checked 2026-06-30 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 · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-19 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) · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-19 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 · checked 2026-06-30 correlational usage data, not proof.

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

Core source records

This table lists the page’s core content records and when they were checked. Claim-specific citations appear beside the relevant text and may not be repeated here.

ID	Supports	Source	Checked
CIT-01	Supports occupation-level wage context for AI Research Scientist.	AI Research Scientist BLS OEWS wage source	2026-06-25T08:28:38+00:00
CIT-02	Supports occupation-level outlook context for AI Research Scientist.	AI Research Scientist BLS Employment Projections source	2026-06-25T08:27:06+00:00
CIT-03	Supports skills, tasks, interests, and fit context for AI Research Scientist.	AI Research Scientist O*NET source	2026-07-14T04:32:12+00:00

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