Vier relative Bänder (Niedrig / Mittel / Hoch / Sehr hoch)
831 detaillierte Berufe in der BLS-Beschäftigungsprojektionstabelle. Der Wert wird je Code der National Employment Matrix (NEM) vergeben, daher erhalten Berufe mit demselben NEM-Code dasselbe Band
Index der Exposition gegenüber generativer KI, 0–1 wie veröffentlicht
ISCO-08-Berufsgattung — alle Berufe mit demselben Code erhalten diesen Wert
Von dieser Website berechnet, nicht von der IAO veröffentlicht: Von den 1.012 Berufen, die diese Website mit dem IAO-Datensatz verknüpft, erreichen oder übertreffen 7% diesen Wert.
Die Kategorie von BLS ist ein relativer Rang und kein absolutes Niveau, und sie ist keine eigene Messung: Sie fasst die Perzentilränge des Berufs aus mehreren veröffentlichten Studien in vier Bänder zusammen. Sie ist weder eine Beschäftigungs- oder Lohnprognose noch eine Einführungswahrscheinlichkeit und unterscheidet nicht zwischen Automatisierung und Augmentierung.
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.
Task
βE1 + 0.5 × E2
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.Details
O*NET Task ID 21824
1.0
Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.Details
O*NET Task ID 21825
1.0
Clean and manipulate raw data using statistical software.Details
O*NET Task ID 21826
1.0
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.Details
O*NET Task ID 21827
1.0
Design surveys, opinion polls, or other instruments to collect data.Details
O*NET Task ID 21830
1.0
Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.Details
O*NET Task ID 21834
1.0
Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.Details
O*NET Task ID 21837
1.0
Write new functions or applications in programming languages to conduct analyses.Details
O*NET Task ID 21838
1.0
Analyze, manipulate, or process large sets of data using statistical software.Details
O*NET Task ID 21823
0.5
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.Details
O*NET Task ID 21828
0.5
Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.Details
O*NET Task ID 21829
0.5
Identify business problems or management objectives that can be addressed through data analysis.Details
O*NET Task ID 21831
0.5
Identify relationships and trends or any factors that could affect the results of research.Details
O*NET Task ID 21832
0.5
Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.Details
O*NET Task ID 21833
0.5
Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.Details
O*NET Task ID 21835
0.5
Recommend data-driven solutions to key stakeholders.Details
O*NET Task ID 21836
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
ILO occupational description
Statistical, Mathematical and Related Associate Professionals
Nicht akademische statistische, mathematische und verwandte Fachkräfte
Source:European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
Statistical, mathematical and related associate professionals assist in planning the collection, processing and presentation of statistical, mathematical or actuarial data and in carrying out these operations, usually working under the guidance of statisticians, mathematicians and actuaries.
Tasks
(a) assisting in planning and performing statistical, mathematical, actuarial and related calculations;
(b) preparing detailed estimates of quantities and costs of materials and labour required for statistical census and survey operations;
(c) performing technical tasks connected with establishing, maintaining and using registers and sampling frames for census and survey operations;
(d) performing technical tasks connected with data collection and quality control operations in censuses and surveys;
(e) using standard computer software packages to perform mathematical, actuarial, statistical, accounting and related calculations;
(f) preparing statistical, mathematical, actuarial, accounting and other results for presentation in graphical or tabular form;
(g) applying knowledge of statistical, mathematical, actuarial, accounting and related principles and practices in order to identify and solve problems arising in the course of their work;
Versicherungsmathematische Assistenten recherchieren statistische Daten, um Prämiensätze festzulegen und Versicherungspolicen zu gestalten. Sie bewerten die Wahrscheinlichkeit von Unfällen sowie Personen- und Sachschäden anhand statistischer Formeln und Modelle.
Statistische Assistenten sammeln Daten und verwenden statistische Formeln, um statistische Studien durchzuführen und Berichte zu erstellen. Sie erstellen Tabellen, Schaubilder und Erhebungen.
Source:European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
ESCO reuse terms and the modification notice are in Credits.
Shown in the original Korean. This dataset is published under KOGL Type 4, which prohibits derivative works including translation, so the text is reproduced unaltered.
The KNOW occupations and KECO codes listed here were linked to this page's occupation by this site; this is not a correspondence defined by the Korea Employment Information Service or the Ministry of Employment and Labor.
Projected change: +34.6%
Median wage: $120K
lowest $31Kmedian wage across 825 detailed occupations, BLS EP 2025–35highest $559K
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).
März 2026: Dallas Fed: Data scientists classified as high AI-exposure occupation. Wage premiums rising as AI amplifies analytical productivity for experienced workers.
