Market signal
Independent published positions, read together — not a forecast.
- Growing fast employment outlook (+33.5% by 2034)
- 23,400 openings/yr
- High AI exposure
- Median pay $112,590/yr
Data Scientists
Occupation · SOC 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.
Also called: Analytics Consultant · Applied Scientist · Data Analyst · Data Analytic Scientist · Data Analytics Manager · Data Analytics Scientist · Data Analytics Specialist · Data Architect · Data Consultant · Data Economist · Data Engineer · Data Management Scientist
Job family: Computer and Mathematical Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-15-2051-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
99th-percentile task overlap — yet about 23,400 openings a year (+33.5% projected, BLS) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 98th | 0.4 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.5), with simple added tooling (β 0.8), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +33.5% by 2034 |
| Projected annual openings | 23,400 |
| Employment 2024 → 2034 | 245,900 → 328,300 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Tasks
All 16 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Analyze, manipulate, or process large sets of data using statistical software.
- Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
- Design surveys, opinion polls, or other instruments to collect data.
- Identify business problems or management objectives that can be addressed through data analysis.
- Identify relationships and trends or any factors that could affect the results of research.
- Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
- Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
- Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
- Recommend data-driven solutions to key stakeholders.
- Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
- Write new functions or applications in programming languages to conduct analyses.
Work activities
- Analyze data to inform operational decisions or activities.
- Analyze business or financial data.
- Determine appropriate methods for data analysis.
- Prepare data for analysis.
- Apply mathematical principles or statistical approaches to solve problems in scientific or applied fields.
- Prepare graphics or other visual representations of information.
- Prepare analytical reports.
- Present research results to others.
- Develop procedures to evaluate organizational activities.
- Select resources needed to accomplish tasks.
- Analyze data to identify trends or relationships among variables.
- Analyze data to identify or resolve operational problems.
- Update technical knowledge.
- Advise others on analytical techniques.
- Develop scientific or mathematical models.
- Write computer programming code.
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
Showing the top 40 of 87.
How to get in
- Job zone
- Zone 4 — Job Zone Four: Considerable Preparation Needed
- Education
- Most of these occupations require a four-year bachelor's degree, but some do not.
- Typical entry-level education
- Bachelor's degree · BLS, the typical path — not a requirement
- Related experience
- A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
- Preparation level
- SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.
What to study: Biological and Biomedical Sciences , Business, Management, Marketing, and Related Support Services , Computer and Information Sciences and Support Services , Mathematics and Statistics , Multi/Interdisciplinary Studies , Physical Sciences , Social Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Investigative | 7.0 | |
| Conventional | 5.4 | |
| Artistic | 2.6 | |
| Realistic | 2.2 |
Interest areas
| Mathematics/Statistics | 6.8 | |
| Information Technology | 5.8 | |
| Accounting | 2.5 | |
| Finance | 2.3 | |
| Health Care Service | 2.2 | |
| Public Speaking | 2.1 |
Work styles
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 | |
| Intellectual Curiosity | 3.0 | |
| Innovation | 2.4 | |
| Achievement Orientation | 2.1 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $63,650 |
| 25th percentile | $82,630 |
| Median (50th) | $112,590 |
| 75th percentile | $155,810 |
| 90th percentile | $194,410 |
| People employed | 233,440 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 69,410 | $117,020 |
| Finance and Insurance · Sector | 41,020 | $123,570 |
| Information · Sector | 26,840 | $137,600 |
| Management of Companies and Enterprises · Sector | 26,100 | $126,940 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 14,390 | $100,780 |
| Health Care and Social Assistance · Sector | 13,070 | $87,870 |
| Manufacturing · Sector | 9,240 | $118,080 |
| Educational Services · Sector | 8,700 | $79,310 |
| Wholesale Trade · Sector | 7,680 | $110,930 |
| Direct Health and Medical Insurance Carriers · National industry | 6,620 | $104,950 |
| Temporary Help Services · National industry | 5,290 | $99,840 |
| Engineering Services · National industry | 3,580 | $105,400 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Direct Health and Medical Insurance Carriers · National industry | 9.74× | 6,620 |
| Research and Development in the Social Sciences and Humanities · National industry | 8.04× | 740 |
| Management of Companies and Enterprises · Sector | 6.14× | 26,100 |
| Information · Sector | 6.1× | 26,840 |
| Finance and Insurance · Sector | 4.35× | 41,020 |
| Professional, Scientific, and Technical Services · Sector | 4.26× | 69,410 |
| Engineering Services · National industry | 2.05× | 3,580 |
| Insurance Agencies and Brokerages · National industry | 1.86× | 2,790 |
Related occupations
Part of the Digital Technology , Financial Services and Marketing & Sales career clusters.
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Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Data Scientists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- Bachelor's degree · typical entry level
- Job zone 4 — Job Zone Four: Considerable Preparation Needed · preparation tier
- Biological and Biomedical Sciences · field of study
- Business, Management, Marketing, and Related Support Services · field of study
Zoom out
See where this work sits in the bigger picture.
Write a report on thisheadline · factoids · citation
Data Scientists show 99th-percentile AI task overlap — and about 23,400 annual U.S. openings
- Data Scientists rank in the 99th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 23,400 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be growing fast (+33.5%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $112,590, across about 233,440 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Data Scientists show 99th-percentile AI task overlap — and about 23,400 annual U.S. openings • Data Scientists rank in the 99th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 23,400 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+33.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $112,590, across about 233,440 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Data Scientists". https://singulariki.com/roles/role-15-2051-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Data Scientists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026. https://singulariki.com/roles/role-15-2051-00
APA
Singulariki. (2026). Data Scientists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-15-2051-00
BibTeX
@misc{singulariki-role-15-2051-00,
title = {Data Scientists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-15-2051-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.