クオンツアナリスト
ビジネス・金融AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分(低い / 中程度 / 高い / 非常に高い)
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.461
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.44
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは28%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 30 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare appropriate formatting to data sets as requested.15-2041 | 0.108615.7 | 0.362522.3 |
Evaluate sources of information to determine any limitations in terms of reliability or usability.15-2041 | 0.100414.5 | 0.288217.7 |
Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data.15-2041 | 0.087412.6 | 0.546633.6 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices.15-2041 | 0.03555.1 | 0.00230.1 |
Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.15-2041 | 0.03424.9 | 0.02811.7 |
Report results of statistical analyses, including information in the form of graphs, charts, and tables.15-2041 | 0.02894.2 | 0.02071.3 |
Write program code to analyze data using statistical analysis software.15-2041 | 0.02824.1 | 0.05053.1 |
Develop software applications or programming to use for statistical modeling and graphic analysis.15-2041 | 0.02733.9 | 0.01480.9 |
Develop and test experimental designs, sampling techniques, and analytical methods.15-2041 | 0.02713.9 | 0.00660.4 |
Process large amounts of data for statistical modeling and graphic analysis, using computers.15-2041 | 0.02613.8 | 0.02301.4 |
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