Key Takeaways
- 5.7% year-over-year growth in U.S. employment for computer and mathematical occupations in 2023 (from the prior year).
- 4,975,000 people employed in computer and mathematical occupations in the United States (2023).
- 19.5% of all STEM workers in the United States were in computer and mathematical occupations (2022).
- 1.0% of total U.S. employment was in computer and mathematical occupation categories, showing tight labor demand (May 2023 share).
- 4.7% of U.S. technology workers reported being unemployed in 2023 (unemployment rate for IT occupation group).
- 11.3% annual growth in job openings for data scientists in the United States (2021–2023 trend).
- The median pay for data scientists in the U.S. was $108,020 (2023).
- The median pay for information security analysts in the U.S. was $120,360 (2023).
- The median pay for computer systems analysts in the U.S. was $99,270 (2023).
- 12.9% of the U.S. technology workforce is Black or African American (2023 ACS-based estimates).
- 25.5% of the U.S. technology workforce is Hispanic or Latino (2023 ACS-based estimates).
- 27.6% of U.S. STEM workers are women (2022).
- 30% of U.S. knowledge workers were fully remote in 2023 (BLS or survey estimate for remote).
- In 2023, 56% of U.S. tech workers reported hybrid work as their primary work arrangement (survey).
- In 2023, 39% of U.S. employees in the IT sector said they could work fully remote (survey).
U.S. tech jobs are growing fast with tight hiring demand, rising AI and cybersecurity needs, and expanding hybrid work.
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How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Megan Gallagher. (2026, February 13). Tech Industry Employment Statistics. Gitnux. https://gitnux.org/tech-industry-employment-statistics
Megan Gallagher. "Tech Industry Employment Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/tech-industry-employment-statistics.
Megan Gallagher. 2026. "Tech Industry Employment Statistics." Gitnux. https://gitnux.org/tech-industry-employment-statistics.
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