Datenqualitaetsanalysten
Computer und MathematikKI-Exposition
- Datenquelle: BLSVeröffentlicht: 2026-08
Sehr hoch· relativ
NiedrigVier relative BänderSehr hochWert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
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
- Datenquelle: AnthropicVeröffentlicht: 2026-03
0.461
0.000Spannweite der hier geführten Werte0.745Skala, Bezugsmenge und Quelle
Index der beobachteten Exposition, 0–1 wie veröffentlicht
Auf O*NET-Aufgaben abgebildet
- Datenquelle: ILOVeröffentlicht: 2025
0.57
0.09Spannweite der hier geführten Werte0.70Wert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
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.
Welche Art von Wert diese Quelle veröffentlicht
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
Nur exponierte Aufgaben| Task | Claude.aiRaw / share % |
|---|---|
Evaluate sources of information to determine any limitations in terms of reliability or usability.15-2041 | 0.399527.4 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices.15-2041 | 0.13999.6 |
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.15-2041 | 0.09986.8 |
Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.15-2041 | 0.09396.4 |
Draw conclusions or make predictions based on data summaries or statistical analyses.15-2041 | 0.08375.7 |
Prepare appropriate formatting to data sets as requested.15-2041 | 0.07595.2 |
Report results of statistical analyses, including information in the form of graphs, charts, and tables.15-2041 | 0.07585.2 |
Process large amounts of data for statistical modeling and graphic analysis, using computers.15-2041 | 0.05743.9 |
Identify relationships and trends in data, as well as any factors that could affect the results of research.15-2041 | 0.05343.7 |
Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data.15-2041 | 0.05053.5 |
| Not observed on any surface — 28 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. | |
Analyze and interpret statistical data to identify significant differences in relationships among sources of information. | —0 |
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy. | —0 |
Plan data collection methods for specific projects and determine the types and sizes of sample groups to be used. | —0 |
Supervise and provide instructions for workers collecting and tabulating data. | —0 |
Apply sampling techniques or use complete enumeration bases to determine and define groups to be surveyed. | —0 |
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering. | —0 |
Teach graduate or continuing education courses or seminars in biostatistics. | —0 |
Prepare statistical data for inclusion in reports to data monitoring committees, federal regulatory agencies, managers, or clients. | —0 |
Calculate sample size requirements for clinical studies. | —0 |
Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies. | —0 |
Assign work to biostatistical assistants or programmers. | —0 |
Plan or direct research studies related to life sciences. | —0 |
Monitor clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data collected. | —0 |
Develop or use mathematical models to track changes in biological phenomena such as the spread of infectious diseases. | —0 |
Design or maintain databases of biological data. | —0 |
Collect data through surveys or experimentation. | —0 |
Analyze archival data such as birth, death, and disease records. | —0 |
Review clinical or other medical research protocols and recommend appropriate statistical analyses. | —0 |
Provide biostatistical consultation to clients or colleagues. | —0 |
Design surveys to assess health issues. | —0 |
Develop technical specifications for data management programming and communicate needs to information technology staff. | —0 |
Write work instruction manuals, data capture guidelines, or standard operating procedures. | —0 |
Track the flow of work forms including in-house data flow or electronic forms transfer. | —0 |
Train staff on technical procedures or software program usage. | —0 |
Supervise the work of data management project staff. | —0 |
Monitor work productivity or quality to ensure compliance with standard operating procedures. | —0 |
Generate data queries based on validation checks or errors and omissions identified during data entry to resolve identified problems. | —0 |
Develop project-specific data management plans that address areas such as data coding, reporting, or transfer, database locks, and work flow processes. | —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 |
|---|---|
Prepare appropriate formatting to data sets as requested.O*NET Task ID 16268 | 1.0 |
Develop technical specifications for data management programming and communicate needs to information technology staff.O*NET Task ID 16270 | 1.0 |
Develop or select specific software programs for various research scenarios.O*NET Task ID 16271 | 1.0 |
Write work instruction manuals, data capture guidelines, or standard operating procedures.O*NET Task ID 16273 | 1.0 |
Design and validate clinical databases, including designing or testing logic checks.O*NET Task ID 16281 | 1.0 |
Design forms for receiving, processing, or tracking data.O*NET Task ID 16286 | 1.0 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices.O*NET Task ID 16266 | 0.5 |
Provide support and information to functional areas such as marketing, clinical monitoring, and medical affairs.O*NET Task ID 16267 | 0.5 |
Evaluate processes and technologies, and suggest revisions to increase productivity and efficiency.O*NET Task ID 16269 | 0.5 |
Contribute to the compilation, organization, and production of protocols, clinical study reports, regulatory submissions, or other controlled documentation.O*NET Task ID 16272 | 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
Berufsinformationen
Aktuelle Veränderungen mit Bezug zu diesem Beruf
Aug. 2026: 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.
[Quelle: U.S. Bureau of Labor Statistics]These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.