Data Scientists
Computer and Mathematical Occupations- O*NET-SOC 代码
- 15-2051.00
Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.
- 数据来源: BLS发布时间: 2026-08
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: 2026-03
0.461
0.000此处所载数值的范围0.745职业群单位数值
尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
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
按 ISCO-08 细类（4 位）发布的数值。按原样应用美国劳工统计局（BLS）的官方对照表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）关联到本职业，对应关系为全部或部分对应
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
已评分任务 16 项 · β ≥ 0.5 的任务 16 项（100.0%）
β = 直接暴露（E1）+ 0.5 × 借助工具时的暴露（E2），依据原始代码库的定义。
- 原始单位：O*NET 27.2 任务 → O*NET 31.0 职业代码
- 所有已评分任务均在 O*NET 31.0 任务列表中。
- 来源
- OpenAI "GPTs are GPTs" exposure rubric
- 版本
- gh-main-0471612
- 许可
- MIT License, Copyright (c) 2024 OpenAI
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 (O*NET 27.2) | 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 |
Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.O*NET Task ID 21829 | 0.5 |
Identify business problems or management objectives that can be addressed through data analysis.O*NET Task ID 21831 | 0.5 |
Identify relationships and trends or any factors that could affect the results of research.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.O*NET Task ID 21833 | 0.5 |
Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.O*NET Task ID 21835 | 0.5 |
Recommend data-driven solutions to key stakeholders.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
O*NET 31.0 全部任务（16 项）
本职业在 O*NET 31.0 中的全部任务（英文原文；任务类型为核心或补充）。暴露值不列在此清单中，而在上方表格里，位于各来源所用 O*NET 版本的任务表述旁。
| 任务（O*NET 31.0） | 类型 |
|---|---|
| Analyze, manipulate, or process large sets of data using statistical software. | 核心 |
| Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use. | 核心 |
| Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods. | 核心 |
| Clean and manipulate raw data using statistical software. | 核心 |
| Compare models using statistical performance metrics, such as loss functions or proportion of explained variance. | 核心 |
| Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software. | 核心 |
| Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users. | 核心 |
| Identify business problems or management objectives that can be addressed through data analysis. | 核心 |
| Identify relationships and trends or any factors that could affect the results of research. | 核心 |
| Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis. | 核心 |
| Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques. | 核心 |
| Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies. | 核心 |
| Recommend data-driven solutions to key stakeholders. | 核心 |
| Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest. | 核心 |
| Write new functions or applications in programming languages to conduct analyses. | 核心 |
| Design surveys, opinion polls, or other instruments to collect data. | 补充 |
0.00/ 3· 机器人暴露指数
尺度 · 母数 · 来源
对每项体力任务，按已实际部署、销售或演示的机器人能够完成它的环境评定 0–3 级（0 不可 · 1 围栏式机器单元、灌装线等机器人专用设施 · 2 医院药房、集装箱港口等受管理的设施 · 3 私人住宅、建筑工地等非结构化环境），再按工作时间占比求平均
以该职业 O*NET 29.3 任务中的体力任务为准
等级与时间占比由 Anthropic 使用 Claude Opus 5（网络搜索）估算（截至 2026 年）。
机器人暴露指数与本站的 AI 暴露度是不同的指标，不能相加或相互比较。该指数不代表实际采用情况或成本竞争力，0 也包括没有体力任务的职业。
每项体力任务的等级
该职业的 O*NET 29.3 任务中没有被归类为体力任务的任务。
职业信息
就业展望（BLS）
这些数值属于整个 SOC 15-2051 组（Data scientists），并非仅属于该 O*NET 职业。
- 就业人数，2025
- 276K
- 预计就业人数，2035
- 371K
- 变化 (2025–35)
- +34.6%
- 年薪中位数，2025
- $120,230
- 入职通常所需学历
- Bachelor's degree
韩国职业分类关联（KSCO）
来自 AI Changing Work 制作的 Korea Occupation Direct Crosswalk v1.1 当前版本的关联。关系值从 O*NET-SOC 职业一侧解读。
| KSCO 代码 | KSCO 名称 | 关系 | 状态 |
|---|---|---|---|
| 22420 | 데이터 분석가 | 范围相同 | 已确认 |
关系与状态的定义(kr-direct v1.1)
国际劳工组织（ILO）职业说明
以下 ISCO-08 职业组由 AI Changing Work 原样套用美国劳工统计局（BLS）发布的对照表（2018 SOC→2010 SOC、2010 SOC→ISCO-08）、不逐案取舍，与本职业相关联；每项关联可能是完全对应，也可能是部分对应。
- ISCO-08 3314Statistical, Mathematical and Related Associate Professionals
统计、数学及有关助理专业人员
自行翻译
自行翻译 · 非 ISCO 官方词汇 — 未经国际劳工组织审核。
以下定义、任务和职业示例是 ILO 对该 ISCO-08 职业组的描述，而不是对本职业的描述。
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.
任务
- (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;
- (h) supervising the work of statistical clerks.
职业示例
Actuarial assistant · Mathematical assistant · Statistical assistant
Sources: ILO ISCO-08 — details: Credits
ESCO 职业词汇
以下每一项都是在已公开的ESCO-O*NET Crosswalk（第 1 版，2022 年）中与本职业逐项对应的。对应类型出自该对照表；表述出自 AI Changing Work。
- data scientistESCO 2511.3范围相同
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
- ICT intelligent systems designerESCO 2511.10本职业范围更宽
ICT intelligent systems designers apply methods of artificial intelligence in engineering, robotics and computer science to design programs which simulate intelligence including thinking models, cognitive and knowledge-based systems, problem solving, and decision making. They also integrate structured knowledge into computer systems (ontologies, knowledge bases) in order to solve complex problems normally requiring a high level of human expertise or artificial intelligence methods.
- data analystESCO 2511.2有重叠，但不相同
Data analysts import, inspect, clean, transform, validate, model, or interpret collections of data with regard to the business goals of the company. They ensure that the data sources and repositories provide consistent and reliable data. Data analysts use different algorithms and IT tools as demanded by the situation and the current data. They might prepare reports in the form of visualisations such as graphs, charts, and dashboards.
- knowledge engineerESCO 2529.8有重叠，但不相同
Knowledge engineers integrate structured knowledge into computer systems (knowledge bases) in order to solve complex problems normally requiring a high level of human expertise or artificial intelligence methods. They are also responsible for eliciting or extracting knowledge from information sources, maintaining this knowledge, and making it available to the organisation or users. To achieve this, they are aware of knowledge representation and maintenance techniques (rules, frames, semantic nets, ontologies) and use knowledge extraction techniques and tools. They can design and build expert or artificial intelligence systems that use this knowledge.
- statisticianESCO 2120.6有重叠，但不相同
Statisticians collect, tabulate, and, most importantly, analyse quantitative information coming from a varied array of fields. They interpret and analyse statistical studies on fields such as health, demographics, finance, business, etc. and advise based on patterns and drawn analysis.
来源: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
许可: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
法国职业分类关联（ROME）
本职业与 ROME 4.0 职业条目（fiche）之间的关联，由 AI Changing Work 借助 AI 模型判定。关系从本职业一侧解读。标为「暂定」的关联仅凭同一模型系列（Claude）两位判定者的判定采纳，或对其判定适用运营方规则后采纳，因此其依据弱于已确认的关联。
| ROME 代码 | ROME 名称（法语） | 关系 |
|---|---|---|
| M1405 | Data scientist | 范围相同暂定 |
| G1211 | Analyste de la performance sportive | 相关，但不对应 |
| M1423 | Chief Data Officer | 相关，但不对应 |
| M1414 | Ingénieur statisticien / Ingénieure statisticienne | 相关，但不对应暂定 |
| M1419 | Data analyst | 相关，但不对应暂定 |
| M1811 | Data engineer | 相关，但不对应暂定 |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
西班牙职业分类关联（CNO-11）
本职业与 CNO-11 职业之间的关联，由 AI Changing Work 借助 AI 模型判定。关系从本职业一侧解读。
| CNO-11 代码 | CNO-11 名称（西班牙语） | 关系 |
|---|---|---|
| 2416 | Estadísticos | 相关，但不对应暂定 |
| 2729 | Especialistas en bases de datos y en redes informáticas no clasificados bajo otros epígrafes | 相关，但不对应暂定 |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
德国职业分类关联（KldB）
本职业与 KldB 职业之间的关联，由 AI Changing Work 借助 AI 模型判定。关系从本职业一侧解读。
| KldB 代码 | KldB 名称（德语） | 关系 |
|---|---|---|
| 43104 | Berufe in der Informatik (ohne Spezialisierung) - hoch komplexe Tätigkeiten | 本职业范围更窄 |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
日本职业分类关联（JSCO）
本职业与 JSCO 职业之间的关联，由 AI Changing Work 借助 AI 模型判定。关系从本职业一侧解读。
| JSCO 代码 | JSCO 名称（日语） | 关系 |
|---|---|---|
| 119 | その他の技術者 | 本职业范围更窄 |
| 109 | その他の情報処理・通信技術者 | 相关，但不对应 |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
与本职业相关的近期变化
摘要和关联标注由 AI Changing Work 撰写和判断；数字请以所链接的来源为准。
直接相关 BLS Occupational Outlook Handbook (updated Aug 2026): employment of data scientists is projected to grow 35% from 2025 to 2035, much faster than the average for all occupations, with about 24,800 openings a year.
[来源: U.S. Bureau of Labor Statistics]
本页数据的来源与许可，以及 AI Changing Work 的修改与翻译说明：致谢与来源