Data Scientists
In short: today's best AI models could do about 71% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 95% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 5% of the time, stays with people.
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.
262,440 people do this job in the US. $120,230 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on2 h
- A person checks AI's work26 h
- Desk work AI can't do yet10 h
- Stays with people2 h
Data Scientists, now: of a 40-hour week, AI could take on 2 hours, 26 hours become checking its work, 10 hours are desk work it can't do yet, and 2 hours stay with people.
The largest pieces: Analyze large data sets (about 6 h, a person checks); Clean raw data (about 3 h, a person checks); Find relationships in the data (about 3 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 95%
- 0%
- 4%
- 0%
- 0%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 71% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 12%. Today's estimate: 71%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.66% went to this job's tasks in May 2026, the 37th largest of the 447 jobs that showed up at all; 9 of its 16 tasks showed up. Within each of Anthropic's methods, never across a change: 0.77% to 0.66% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 16 |
| Released March 2025 | none | n/a | 0 of 16 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 16 |
| The week of Nov 13, 2025 (released January 2026) | none | n/a | 0 of 16 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 16 |
| April 2026 (released June 2026) | 0.77% | 34 of 434 | 10 of 16 |
| May 2026 (released June 2026) | 0.66% | 37 of 447 | 9 of 16 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates almost none of this job's working time as physical work, so robots do not reach it.
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
4% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Find problems analysis can solve, the conversation, 2%
- Present analysis results, the conversation, 1%
- Recommend solutions to stakeholders, the conversation, 1%
What becomes checking the machine
89% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 6% is desk work that is small if wrong, which can be handed over with lighter checks.
- Analyze large data sets, costly if wrong, 17%
- Validate and refine models, costly if wrong, 17%
- Clean raw data, costly if wrong, 12%
- Find relationships in the data, costly if wrong, 9%
- Turn analysis into business answers, costly if wrong, 7%
- Write analysis code, costly if wrong, 6%
What to learn
- Briefing a model and checking its drafts. About 95% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 16 tasks from .
| Task | ||||
|---|---|---|---|---|
| Analyze, manipulate, or process large sets of data using statistical software.Coding and analysis at the desk.Done daily by most who do it. | 17% | 4.2 h1.7 h to 1.7 work days | Documents 100% | 86%Half by now |
| Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.Modeling at the desk.Done more than monthly by most who do it. | 17% | 1 work day2.5 h to 4.2 work days | Documents 100% | 43%Half by Dec 2026 |
| Clean and manipulate raw data using statistical software.Coding at the desk.Done daily by most who do it. | 12% | 3.3 h50 min to 1.7 work days | Documents 100% | 71%Half by now |
| Identify relationships and trends or any factors that could affect the results of research.Analysis at the desk.Done more than weekly by most who do it. AI observed on this task. | 9% | 4.2 h1.3 h to 1.7 work days | Documents 100% | 77%Half by now |
| Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.Analysis and the write up.Done more than yearly by most who do it. | 7% | 5 h1.7 h to 2.1 work days | Documents 100% | 73%Half by now |
| Write new functions or applications in programming languages to conduct analyses.Coding at the desk.Done more than weekly by most who do it. | 6% | 4.2 h1.7 h to 1.7 work days | Documents 100% | 86%Half by now |
| Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.Analysis at the desk.Done more than weekly by most who do it. | 6% | 2.5 h50 min to 1 work day | Documents 100% | 92%Half by now |
| Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.Charting in the software.Done several times a day by most who do it. | 5% | 1.7 h38 min to 6.7 h | Documents 100% | 96%Half by now |
| Identify business problems or management objectives that can be addressed through data analysis.Conversations with the business and the framing written.Done more than monthly by most who do it. | 5% | 3.3 h50 min to 1.3 work days | Documents 60%, conversation 40% | 48%Desk part half by now |
| Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.Modeling at the desk.Done more than monthly by most who do it. | 4% | 4.2 h1.7 h to 1 work day | Documents 100% | 97%Half by now |
| Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.Analysis at the desk.Done yearly or less by most who do it. | 3% | 1 work day2.5 h to 4.2 work days | Documents 100% | 43%Half by Dec 2026 |
| Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.Slides written and presented.Done more than monthly by most who do it. | 2% | 3.3 h50 min to 1.3 work days | Documents 50%, conversation 50% | 40%Desk part half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Data Scientists is within reach of AI now?
- An estimated 71% of the working time is within reach of AI models now and 95% by the end of 2028, on METR's long-run pace at four in five (93% to 95% across the three paces the site shows). Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Data Scientists come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Analyze large data sets (86% of the task's time now, 100% by the end of 2028); Clean raw data (71% of the task's time now, 100% by the end of 2028); and Find relationships in the data (77% of the task's time now, 100% by the end of 2028).
- Which tasks of Data Scientists stay with people?
- Every task has a desk part, so each can come partly within reach; the conversation, in-person and hands-on parts of each task stay with people, about 5% of the working time.
- What should Data Scientists learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are briefing a model and checking its drafts; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 4% of the working time stays with people, 89% becomes checking a model's work and 6% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Data Scientists?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.66% went to this job's tasks, the 37th largest share of the 447 jobs that showed up; 9 of its 16 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 46%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Data Scientists?
- Anthropic's robot exposure rating (September 2026) finds almost none of the working time is physical work, so robots do not reach it.
- How is this estimated?
- From the 16 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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Your own version of this job
The page above is the occupation. Your job is your title and what you actually do: give both, and they are read against these tasks in your browser, nothing sent anywhere. The headline is free: how much of your working time is within reach now and by the end of 2028, and how many of the tasks your description names.
The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.