Job Market Health score
56/ 100
Balanced
Stable
- At least 90% of required inputs and occupation-industry mapping are covered.
Job Market Health scores are derived indicators based on published data, informational only, not a forecast or guarantee.
Data scientists
SOC 15-2051 · 15-0000 · Score reference month August 2026
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.
In short
The national Job Market Health score is in the Balanced band for August 2026.
$120,230Median annual wageOEWS May 2025
24,800Projected annual openingsBLS Employment Projections 2025-2035
+34.6%Projected growth2025–2035
262,440Total employmentOEWS May 2025
Latest interpretation
What stands out for data scientists
The clearest signals from the latest validated score, outlook and metro data.
Long-term outlook is the strongest component
Long-term outlook scores 84/100, compared with 31/100 for current demand, a 53-point difference.
JobMarketHealth score componentsLong-term growth is projected at 10% or more
BLS projects 24,800 average annual openings and +34.6% employment growth from 2025 to 2035. Projected openings are a modeled annual average, not vacancies open today.
BLS Employment ProjectionsLargest published metro market
New York-Newark-Jersey City, NY-NJ has the largest published metro employment count for data scientists, at 23,160 jobs. The next-largest published metro is San Francisco-Oakland-Fremont, CA with 10,460 jobs. This measures employment scale, not market quality.
BLS OEWSLocal market context
QCEW industry conditions for this occupation’s employment mix
Choose a state to see how the industries that employ this occupation are performing locally.
Choose a state above to explore its available industry context.
How to read this section
QCEW measures employment and wages by industry, not by occupation. We weight those industry conditions using the published industry mix for this occupation. These figures describe the industries that employ this occupation; they are not direct counts of people working in the occupation or jobs currently open.
Source: BLS QCEW. This section is not part of the national 0 to 100 Job Market Health score. See the methodology.
Compare states for data scientistsSide-by-side published industry conditions for two or three states. There is no state-level health score; the 0 to 100 score is national.
How to read this page
The score is a national composite from 0 to 100 that weights near term hiring demand at 50 percent, the occupation's own employment fundamentals at 30 percent, and long-term outlook at 20 percent. Wages and occupation profile details are context shown alongside it, not score inputs. See the full methodology.
Median annual wage and metro employment come from BLS OEWS, which is annual. Projected annual openings and projected growth come from BLS Employment Projections and describe the published projection horizon, so they are not a count of jobs open today. Values shown as not published are suppressed by the source rather than filled in here.
Job Market Health trend
Published composite score · 0–100 · by reference month
Latest published values
- Aug 2656 / 100
View values: Job Market Health history
| Reference month | Score / 100 |
|---|---|
| Jan 25 | 61 |
| Feb 25 | 60 |
| Mar 25 | 59 |
| Apr 25 | 60 |
| May 25 | 61 |
| Jun 25 | 59 |
| Jul 25 | 58 |
| Aug 25 | 54 |
| Sep 25 | 55 |
| Oct 25 | 53 |
| Nov 25 | 54 |
| Dec 25 | 53 |
| Jan 26 | 54 |
| Feb 26 | 53 |
| Mar 26 | 53 |
| Apr 26 | 54 |
| May 26 | 56 |
| Jun 26 | 57 |
| Jul 26 | 56 |
| Aug 26 | 56 |
Score components
The three published inputs behind the composite · each 0–100
Largest metro markets by employment
OEWS employment count, where the jobs are, not a quality ranking
OEWS May 2025. These occupation counts describe the survey reference period.
Linked metro names open overall job-market profiles.
| Metro area | Employment | Jobs per 1,000 | Median annual wage |
|---|---|---|---|
| New York-Newark-Jersey City, NY-NJ | 23,160 | 2.44 | $135,980 |
| San Francisco-Oakland-Fremont, CA | 10,460 | 4.41 | $170,110 |
| Dallas-Fort Worth-Arlington, TX | 10,120 | 2.50 | $127,750 |
| Los Angeles-Long Beach-Anaheim, CA | 9,850 | 1.57 | $129,740 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 9,260 | 2.95 | $132,200 |
| Seattle-Tacoma-Bellevue, WA | 8,370 | 4.01 | $164,740 |
| Chicago-Naperville-Elgin, IL-IN | 7,940 | 1.76 | $107,640 |
| Boston-Cambridge-Newton, MA-NH | 7,930 | 2.93 | $132,040 |
| Atlanta-Sandy Springs-Roswell, GA | 6,820 | 2.36 | $108,940 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 6,480 | 2.24 | $109,910 |
Education & training
Typical entry requirements · BLS Employment Projections 2025-2035
Top skills
Occupation profile · O*NET 31.0 Database
- Mathematics
- Critical Thinking
- Reading Comprehension
- Complex Problem Solving
- Programming
- Active Learning
- Speaking
- Active Listening
- Writing
- Judgment and Decision Making
AI & this occupation
How AI relates to Data scientists work
Across 3 research exposure measures, Data scientists is in the higher technical AI exposure band based on the mean of the available source-specific percentile ranks. Exposure describes how much of the work the studied AI capabilities could technically assist with; it is not a forecast of job loss.
Observed use of Claude for this occupation's tasks is in the higher third of occupations in the Anthropic Economic Index, a dated observed task-exposure benchmark, not conversation share or the share of workers using AI. Its observation window is not recorded in this saved release.
JobMarketHealth has not measured an AI effect on this occupation's employment or wages, and these figures do not enter its scores; there is no clear evidence of displacement in the data shown here.
Exposure measures (kept separate)
- GPTs are GPTs exposure (β, GPT-4 rated): 0.75 of 1, national percentile 96 (higher third)
- Microsoft AI applicability: 0.36 of 1, national percentile 98 (higher third)
- OECD AI Capability Gap (lower gap = higher potential exposure): gap 1.7 of 23, national percentile 78 (higher third); mean of O*NET detailed occupations
OECD capability gap by domain
As published by the OECD: the gap between what the occupation demands (0-5) and current AI capability in each domain. A lower gap means AI is closer to the requirement (higher potential exposure); 0 means AI already meets it or the domain is not relevant.
- Language: gap 0.23 (demand 3.23 of 5)
- Social interaction: gap 0.55 (demand 2.55 of 5)
- Problem solving: gap 0.78 (demand 2.77 of 5)
- Creativity: gap 0.00 (demand 2.13 of 5)
- Metacognition & critical thinking: gap 1.13 (demand 3.13 of 5)
- Knowledge, learning & memory: gap 0.13 (demand 3.13 of 5)
- Vision: gap 0.00 (demand 0.25 of 5)
- Manipulation: gap 0.00 (demand 0.00 of 5)
- Robotic intelligence: gap 0.00 (demand 0.00 of 5)
National context: what businesses used generative AI for
From the Census 2025-26 BTOS AI Supplement (all industries; businesses whose employees used generative AI). Census did not measure Data scientists specifically.
- Writing or editing documents, emails, or communications: 85.4%
- Searching for information or technical help: 49.9%
- Interpreting, analyzing, translating, or summarizing documents: 44.6%
- Information processing, paperwork, or filing: 34.7%
Exposure and observed usage are separate measures and neither equals job loss. Census BTOS figures are business-level surveys and are not occupation-specific. Sources: GPTs are GPTs occupation exposure; Microsoft Working with AI: AI applicability; OECD AI Exposure Measure (AI Capability Gap); Anthropic Economic Index; Census BTOS Core AI Use; Census BTOS AI Supplement 2025-26. These figures do not affect Job Market Health scores. Explore AI & Jobs · How we use AI data.
Sources and methodology
The datasets behind the figures on this page
Values marked “Not published” are suppressed or not published by the source. We leave them blank rather than estimating them. Pipeline status for every source is on Data Status.
Related occupations
O*NET® lists these occupations as related. Each link opens the same published view of score, wages and outlook.
Evidence record
JobMarketHealth derived indicator; not a government statistic. The composite score is national and occupation level.
- Occupation
- Data scientists
- SOC code
- 15-2051
- Geography
- United States
- Reference month
- August 2026
- Published Job Market Health score
- 56 / 100
- Restated score
- 56 / 100
- Band
- Balanced
- Current Demand component
- 31 / 10050% weight
- Occupation Fundamentals component
- 79 / 10030% weight
- Long-Term Outlook component
- 84 / 10020% weight
- Confidence
- HighAt least 90% of required inputs and occupation-industry mapping are covered.
- Methodology version
- v1.0
- Source vintage: Annual
- EP-2025-2035|CPS-25b-2025|OEWS-2020-2025
- Source vintage: Monthly calculated at
- 2026-10-02T15:32:07.267Z
- First published at
- Sep 29, 2026
- Recalculated at
- Oct 2, 2026
- Canonical record URL
- https://jobmarkethealth.com/occupations/data-scientists#evidence-record
- Derived by
- JobMarketHealth
MethodologyBLS JOLTSBLS CESBLS Employment ProjectionsHow to citeNational Job Market Health release, August 2026
Keep going
Learn how to judge a market from these numbers in how to tell if a job market is healthy, or why projected growth and annual openings often disagree in projected growth vs projected annual openings. Coverage, revisions and limits are explained on about the data.
The datasets behind these figures, and what each one can and cannot show, are set out on data sources.