أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)
831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه
مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر
وحدة التصنيف ISCO-08 — تأخذ كل المهن ذات الرمز نفسه هذه القيمة
محسوب من قِبل هذا الموقع وليس منشورًا من منظمة العمل الدولية: من بين 1,012 مهنة يربطها هذا الموقع بمجموعة بيانات المنظمة، تبلغ نسبة التي تساوي هذه القيمة أو تتجاوزها 7%.
فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.
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
اختصاصيون مساعدون في الإحصاء والرياضيات ومن يرتبط بهم
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;
يقوم المساعدون الأكتواريون بإجراء بحث بيانات إحصائية من أجل تحديد أسعار الأقساط وسياسات التأمين. ويراجعون إمكانية وقوع الحوادث والإصابات وتلف العقارات باستخدام الصيغ والنماذج الإحصائية.
مساعد إحصائي / مساعدة إحصائيةESCO 3314.2
يجمع المساعدون الإحصائيون البيانات ويستخدمون الصيغ الإحصائية لتنفيذ الدراسات الإحصائية وإنشاء التقارير. ويقومون بإنشاء المخططات والرسوم البيانية والاستبيانات.
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
أبريل 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).
مارس 2026: Dallas Fed: Data scientists classified as high AI-exposure occupation. Wage premiums rising as AI amplifies analytical productivity for experienced workers.
