Which data scientist tasks are most exposed to AI?

Analyze and interpret statistical data to identify significant differences in relationships among sources of information. Report results of statistical analyses, including information in the form of graphs, charts, and tables. Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.

Will AI replace data scientists? What the job data shows

Q: Will AI replace data scientists?

Our estimate puts data scientists at higher exposure: that is how much of the listed work is information processing AI tools can do or speed up, not a forecast that the job disappears. JobRipe doesn't have enough postings yet to show a demand trend.

AI exposure (estimate)

Higher exposure

From 35 O*NET tasks

Hiring demand on JobRipe

Demand trend: not enough data yet

Open jobs now

1,631

from 575 employers

An estimate, not a prediction. This is an estimate of how much of this kind of work AI tools can do or speed up, built from published task lists. It is not a prediction about your job, your employer or your career: most jobs change rather than disappear, and the parts that change are often the routine ones.

What the work is made of

Information work (reading, writing, calculating, analysing)about 60%
Planning, deciding and coordinatingabout 40%
Working directly with peopleabout 10%
Hands-on physical workabout 0%

Tasks AI tools can do or speed up

Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
Report results of statistical analyses, including information in the form of graphs, charts, and tables.
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
Develop and test experimental designs, sampling techniques, and analytical methods.

Tasks that rely on people or hands-on work

Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.
Supervise and provide instructions for workers collecting and tabulating data.
Prepare and structure data warehouses for storing data.

Tasks as published by O*NET for Data Scientists, Statisticians.

Skills rising and falling in data scientist job ads

Not enough postings from the same employers in both periods to compare skills yet.

Thinking about a move?

See nearby roles, how many of their skills you already have, what they pay in job ads, and free or government-funded ways to learn the rest.

Plan a career switch See data scientist jobs Data Science & ML salaries

How we work this out

Every task O*NET lists for the occupation is put in one of four groups by its main verb and what it acts on. Information work counts fully towards exposure, planning and deciding half, working directly with people a fifth, and hands-on physical work not at all. Tasks are weighted by how important O*NET's survey of workers rates them. The weighted result is shown only as a band.

Information work (reading, writing, calculating, analysing)
counts fully towards exposure
Planning, deciding and coordinating
counts half towards exposure
Working directly with people
counts a fifth towards exposure
Hands-on physical work
counts not at all towards exposure

Bands: Lower exposure, Medium exposure from 40 in 100 weighted task points, Higher exposure from 65 in 100 weighted task points. Supplemental tasks count 70% of a core task.

Demand: new postings JobRipe first saw from 2026-07-25 to 2026-10-23 compared with the 90 days before (2026-04-26 onwards), only from employers JobRipe was already tracking before 2026-04-26, so adding employers never looks like growth. A trend needs at least 20 earlier postings from 5+ employers; a change of 15% or more is “growing” or “shrinking”. A rising or falling skill needs 10+ postings and a 5-point move.

Sources

O*NET 30.3 Database (task statements, task importance ratings, occupation data), U.S. Department of Labor, Employment and Training Administration (USDOL/ETA) · CC BY 4.0
Autor, Levy & Murnane (2003), The Skill Content of Recent Technological Change, Quarterly Journal of Economics 118(4) · used for the routine / non-routine task framework (method only, no data)
· used for the idea of rating exposure task by task from O*NET (method only, no data)