数据科学家
计算机与数学AI暴露度
- 数据来源: BLS发布时间: '26.08
很高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: '26.03
0.461
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: '25
0.57
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 7%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
显示隐藏的 26 项工作| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.15-2051 | 0.360026.7 | 0.06003.7 |
Generate standard or custom reports summarizing business, financial, or economic data for review by executives, managers, clients, and other stakeholders.15-2051 | 0.180013.3 | 0.330020.1 |
Maintain or update business intelligence tools, databases, dashboards, systems, or methods.15-2051 | 0.150011.1 | 0.230014.0 |
Write new functions or applications in programming languages to conduct analyses.15-2051 | 0.12008.9 | 0.10006.1 |
Prepare data analysis listings and activity, performance, or progress reports.15-2051 | 0.07005.2 | 0.14008.5 |
Synthesize current business intelligence or trend data to support recommendations for action.15-2051 | 0.06004.4 | 0.15009.1 |
Prepare appropriate formatting to data sets as requested.15-2051 | 0.06004.4 | 0.15009.1 |
Provide technical support for existing reports, dashboards, or other tools.15-2051 | 0.05003.7 | 0.02001.2 |
Evaluate processes and technologies, and suggest revisions to increase productivity and efficiency.15-2051 | 0.04003.0 | 0.01000.6 |
Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.15-2051 | 0.04003.0 | 0.00000.0 |
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.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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
与本职业相关的近期变化
2026年4月: 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).
[来源: Bank of Korea Employment Research (2025)]2026年3月: BLS projects 36% growth in data scientist roles through 2034, highest among tech occupations
[来源: U.S. Bureau of Labor Statistics]2026年3月: Dallas Fed: Data scientists classified as high AI-exposure occupation. Wage premiums rising as AI amplifies analytical productivity for experienced workers.
[来源: Dallas Fed (Feb 2026)]