Data Scientists

Computer & Mathematical

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.461

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.57

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 7% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.

O*NET Task ID 21824

1.0
Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.

O*NET Task ID 21825

1.0
Clean and manipulate raw data using statistical software.

O*NET Task ID 21826

1.0
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

O*NET Task ID 21827

1.0
Design surveys, opinion polls, or other instruments to collect data.

O*NET Task ID 21830

1.0
Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.

O*NET Task ID 21834

1.0
Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.

O*NET Task ID 21837

1.0
Write new functions or applications in programming languages to conduct analyses.

O*NET Task ID 21838

1.0
Analyze, manipulate, or process large sets of data using statistical software.

O*NET Task ID 21823

0.5
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

O*NET Task ID 21828

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

Recent Changes Affecting This Occupation

Apr 2026: Bank of Korea research shows junior knowledge workers (≤5 years experience) face 4.0% work hour reduction from AI vs 2.9% for 21+ year veterans. Youth jobs in AI-exposed sectors declined 98.6% of 2.11M total losses (2022-2025).

[Source: Bank of Korea Employment Research (2025)]

Mar 2026: BLS projects 36% growth in data scientist roles through 2034, highest among tech occupations

[Source: U.S. Bureau of Labor Statistics]

Mar 2026: Dallas Fed: Data scientists classified as high AI-exposure occupation. Wage premiums rising as AI amplifies analytical productivity for experienced workers.

[Source: Dallas Fed (Feb 2026)]