Cientistas de Dados
Computação e MatemáticaExposição à IA
- Fonte dos dados: BLSPublicado: '26.08
Muito alto· relativa
BaixoQuatro faixas relativasMuito altoValor por grupo ocupacional
Escala, base e fonte
Quatro faixas relativas (Baixo / Moderado / Alto / Muito alto)
831 ocupações detalhadas da tabela de projeções de emprego do BLS. O valor é atribuído por código da National Employment Matrix (NEM), pelo que as ocupações que partilham um código NEM recebem a mesma banda
- Fonte dos dados: AnthropicPublicado: '26.03
0.461
0.000Intervalo dos valores aqui apresentados0.745Escala, base e fonte
Índice de exposição observada, 0–1 tal como publicado
Mapeado sobre tarefas O*NET
- Fonte dos dados: ILOPublicado: '25
0.57
0.09Intervalo dos valores aqui apresentados0.70Valor por grupo ocupacional
Escala, base e fonte
Índice de exposição à IA generativa, 0–1 tal como publicado
Grupo de base CITP-08 — todas as ocupações com o mesmo código recebem este valor
Calculado por este site, não publicado pela OIT: das 1.012 ocupações que este site liga ao conjunto de dados da OIT, 7% atingem ou superam este valor.
Que tipo de valor esta fonte publica
A categoria de BLS é uma posição relativa, não um nível absoluto, e também não é uma medição de primeira mão: agrupa em quatro faixas as posições percentis da ocupação em vários estudos publicados. Não é uma previsão de emprego ou de salários, não é uma probabilidade de adoção e não distingue automação de aumento.
Task-level exposure
Apenas tarefas expostas| 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 |
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.15-2051 | 0.00000.0 | 0.00000.0 |
Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.15-2051 | 0.00000.0 | 0.00000.0 |
Maintain library of model documents, templates, or other reusable knowledge assets.15-2051 | 0.00000.0 | —0 |
Train staff on technical procedures or software program usage.15-2051 | 0.00000.0 | —0 |
Disseminate information regarding tools, reports, or metadata enhancements. | —0 | 0.00000.0 |
Develop technical specifications for data management programming and communicate needs to information technology staff. | —0 | 0.00000.0 |
Develop project-specific data management plans that address areas such as coding, reporting, or transfer of data, database locks, and work flow processes. | —0 | 0.00000.0 |
| Not observed on any surface — 19 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use. | —0 | —0 |
Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods. | —0 | —0 |
Design surveys, opinion polls, or other instruments to collect data. | —0 | —0 |
Identify business problems or management objectives that can be addressed through data analysis. | —0 | —0 |
Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies. | —0 | —0 |
Communicate with customers, competitors, suppliers, professional organizations, or others to stay abreast of industry or business trends. | —0 | —0 |
Manage timely flow of business intelligence information to users. | —0 | —0 |
Document specifications for business intelligence or information technology reports, dashboards, or other outputs. | —0 | —0 |
Conduct or coordinate tests to ensure that intelligence is consistent with defined needs. | —0 | —0 |
Analyze technology trends to identify markets for future product development or to improve sales of existing products. | —0 | —0 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices. | —0 | —0 |
Contribute to the compilation, organization, and production of protocols, clinical study reports, regulatory submissions, or other controlled documentation. | —0 | —0 |
Track the flow of work forms, including in-house data flow or electronic forms transfer. | —0 | —0 |
Supervise the work of data management project staff. | —0 | —0 |
Design and validate clinical databases, including designing or testing logic checks. | —0 | —0 |
Confer with end users to define or implement clinical system requirements such as data release formats, delivery schedules, and testing protocols. | —0 | —0 |
Design forms for receiving, processing, or tracking data. | —0 | —0 |
Provide support and information to functional areas such as marketing, clinical monitoring, and medical affairs. | —0 | —0 |
Develop or select specific software programs for various research scenarios. | —0 | —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
Alterações recentes que afetam esta ocupação
abr. de 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).
[Fonte: Bank of Korea Employment Research (2025)]mar. de 2026: BLS projects 36% growth in data scientist roles through 2034, highest among tech occupations
[Fonte: U.S. Bureau of Labor Statistics]mar. de 2026: Dallas Fed: Data scientists classified as high AI-exposure occupation. Wage premiums rising as AI amplifies analytical productivity for experienced workers.
[Fonte: Dallas Fed (Feb 2026)]