--- NATIONAL WAGES ---
  total_employment : 435,370
  annual_median    : $116,580
  annual_pct10     : $55,940
  annual_pct25     : $79,370
  annual_pct75     : $157,500
  annual_pct90     : $188,470
  annual_mean      : $122,230
  hourly_median    : $56.05

--- GEOGRAPHIC DISPERSION ---
  highest_state    : District of Columbia ($156,590)
  lowest_state     : Puerto Rico ($60,470)
  dispersion_ratio : 2.590x

--- TOP STATES BY WAGE (54 total) ---
  Professional, Scientific, and Technical Services emp:  123,970  median: $ 121,310
  Federal, State, and Local Government, excluding State and Local Government Schools and Hospitals and the U.S. Postal Service (OEWS Designation) emp:   97,870  median: $ 124,530
  Information                              emp:   48,470  median: $ 131,720
  Finance and Insurance                    emp:   27,020  median: $ 131,760
  Management of Companies and Enterprises  emp:   25,080  median: $ 128,070
  Administrative and Support and Waste Management and Remediation Services emp:   23,450  median: $  99,210
  Manufacturing                            emp:   23,360  median: $ 105,040
  Educational Services                     emp:   17,310  median: $  83,120
  Wholesale Trade                          emp:   12,810  median: $ 109,960
  Health Care and Social Assistance        emp:   10,490  median: $  93,010

--- TOP INDUSTRIES BY EMPLOYMENT (20 total) ---
  Professional, Scientific, and Technical Services emp:  123,970  median: $ 121,310
  Federal, State, and Local Government, excluding State and Local Government Schools and Hospitals and the U.S. Postal Service (OEWS Designation) emp:   97,870  median: $ 124,530
  Information                              emp:   48,470  median: $ 131,720
  Finance and Insurance                    emp:   27,020  median: $ 131,760
  Management of Companies and Enterprises  emp:   25,080  median: $ 128,070
  Administrative and Support and Waste Management and Remediation Services emp:   23,450  median: $  99,210
  Manufacturing                            emp:   23,360  median: $ 105,040
  Educational Services                     emp:   17,310  median: $  83,120
  Wholesale Trade                          emp:   12,810  median: $ 109,960
  Health Care and Social Assistance        emp:   10,490  median: $  93,010

exact_match_status : found
matched_title      : Computer (occupation)
match_score        : 0.7308
wikidata_qid       : Q11202952
word_count         : 2,037
wikipedia_url      : https://en.wikipedia.org/wiki/Computer_(occupation)
license            : CC BY-SA 4.0
fetched_at         : 2026-06-02T20:23:41.223735Z

--- WIKIPEDIA FULL TEXT ---
The term "computer", in use from the early 17th century (the first known written reference dates from 1613), meant "one who computes": a person performing mathematical calculations, before electronic calculators became available. Alan Turing described the "human computer" as someone who is "supposed to be following fixed rules; he has no authority to deviate from them in any detail." Teams of people, often women from the late nineteenth century onwards, were used to undertake long and often tedious calculations; the work was divided so that this could be done in parallel. The same calculations were frequently performed independently by separate teams to check the correctness of the results.
Since the end of the 20th century, the term "human computer" has also been applied to individuals with prodigious powers of mental arithmetic, also known as mental calculators.


== Origins in sciences ==
Astronomers in Renaissance times used that term about as often as they called themselves "mathematicians" for their principal work of calculating the positions of planets. They often hired a "computer" to assist them. For some people, such as Johannes Kepler, assisting a scientist in computation was a temporary position until they moved on to greater advancements. Before he died in 1617, John Napier suggested ways by which "the learned, who perchance may have plenty of pupils and computers" might construct an improved logarithm table.
Computing became more organized when the Frenchman Alexis Claude Clairaut (1713–1765) divided the computation to determine the time of the return of Halley's Comet with two colleagues, Jérôme Lalande and Nicole-Reine Lepaute. Human computers continued plotting the future movements of astronomical objects to create celestial tables for almanacs in the late 1760s. 
The computers working on the Nautical Almanac for the British Admiralty included William Wales, Israel Lyons and Richard Dunthorne. The project was overseen by Nevil Maskelyne. Maskelyne would borrow tables from other sources as often as he could in order to reduce the number of calculations his team of computers had to make. Women were generally excluded, with some exceptions, such as Mary Edwards, who worked from the 1780s to 1815 as one of thirty-five computers for the British Nautical Almanac used for navigation at sea. The United States also worked on their own version of a nautical almanac in the 1840s, with Maria Mitchell being one of the best-known computers on the staff.
Other innovations in human computing included the work done by a group of boys who worked in the Octagon Room of the Royal Greenwich Observatory for Astronomer Royal George Airy. Airy's computers, hired after 1835, could be as young as fifteen, and they were working on a backlog of astronomical data. The way that Airy organized the Octagon Room with a manager, pre-printed computing forms, and standardized methods of calculating and checking results (similar to the way the Nautical Almanac computers operated) would remain a standard for computing operations for the next 80 years.
Women were increasingly involved in computing after 1865. Private companies hired them for computing and to manage office staff.
In the 1870s, the United States Signal Corps created a new way of organizing human computing to track weather patterns. This built on previous work from the US Navy and the Smithsonian meteorological project. The Signal Corps used a small computing staff that processed data that had to be collected quickly and finished in "intensive two-hour shifts". Each individual human computer was responsible for only part of the data. 
In the late nineteenth century Edward Charles Pickering organized the "Harvard Computers". The first woman to approach them, Anna Winlock, asked Harvard Observatory for a computing job in 1875. By 1880, all of the computers working at the Harvard Observatory were women. The standard computer pay started at twenty-five cents an hour. There would be such a huge demand to work there, that some women offered to work for the Harvard Computers for free. Many of the women astronomers from this era were computers with possibly the best-known being Florence Cushman, Henrietta Swan Leavitt, and Annie Jump Cannon, who worked with Pickering from 1888, 1893, and 1896 respectively. Cannon could classify stars at a rate of three per minute. Mina Fleming, one of the Harvard Computers, published The Draper Catalogue of Stellar Spectra in 1890. The catalogue organized stars by spectral lines. The catalogue continued to be expanded by the Harvard Computers and added new stars in successive volumes. Elizabeth Williams was involved in calculations in the search for a new planet, Pluto, at the Lowell Observatory.
In 1893, Francis Galton created the Committee for Conducting Statistical Inquiries into the Measurable Characteristics of Plants and Animals which reported to the Royal Society. The committee used advanced techniques for scientific research and supported the work of several scientists. W.F. Raphael Weldon, the first scientist supported by the committee worked with his wife, Florence Tebb Weldon, who was his computer. Weldon used logarithms and mathematical tables created by August Leopold Crelle and had no calculating machine. Karl Pearson, who had a lab at the University of London, felt that the work Weldon did was "hampered by the committee". However, Pearson did create a mathematical formula that the committee was able to use for data correlation. Pearson brought his correlation formula to his own Biometrics Laboratory. Pearson had volunteer and salaried computers who were both men and women. Alice Lee was one of his salaried computers who worked with histograms and the chi-squared statistics. Pearson also worked with Beatrice and Frances Cave-Brown-Cave. Pearson's lab, by 1906, had mastered the art of mathematical table making.


== Mathematical tables ==
Human computers were used to compile 18th and 19th century Western European mathematical tables, for example those for trigonometry and logarithms. Although these tables were most often known by the names of the principal mathematician involved in the project, such tables were often in fact the work of an army of unknown and unsung computers. Ever more accurate tables to a high degree of precision were needed for navigation and engineering. Approaches differed, but one was to break up the project into a form of piece work completed at home. The computers, often educated middle-class women whom society deemed it unseemly to engage in the professions or go out to work, would receive and send back packets of calculations by post. The Royal Astronomical Society eventually gave space to a new committee, the Mathematical Tables Committee, which was the only professional organization for human computers in 1925.


== Fluid dynamics ==
Human computers were used to predict the effects of building the Afsluitdijk between 1927 and 1932 in the Zuiderzee in the Netherlands. The computer simulation was set up by Hendrik Lorentz.
A visionary application to meteorology can be found in the scientific work of Lewis Fry Richardson who, in 1922, estimated that 64,000 humans could forecast the weather for the whole globe by solving the attending differential primitive equations numerically. Around 1910 he had already used human computers to calculate the stresses inside a masonry dam.


== Wartime computing and electronics ==
It was not until World War I that computing became a profession. "The First World War required large numbers of human computers. Computers on both sides of the war produced map grids, surveying aids, navigation tables and artillery tables. With the men at war, most of these new computers were women and many were college educated." This would happen again during World War II; as more men joined the fight, college educated women were left to fill their positions. One of the first female computers, Elizabeth Webb Wilson, was hired by t

--- SEMANTIC NEIGHBORS (5) ---

  Title: List of obsolete occupations (similarity: 0.5085)
  URL: https://en.wikipedia.org/wiki/List_of_obsolete_occupations
  QID: Q131160772
  Extract: This is a list of obsolete occupations. To be included in this list an occupation must be completely, or to a great extent, obsolete. For example, there are still a few lamplighters retained for ceremonial or tourist purposes, but in the main the occupation is now obsolete. Similarly, there are stil

  Title: Computing education (similarity: 0.5200)
  URL: https://en.wikipedia.org/wiki/Computing_education
  QID: Q85753733
  Extract: Computer science education or computing education is the field of teaching and learning the discipline of computer science and computational thinking. The field of computer science education encompasses a wide range of topics, from basic programming skills to advanced algorithm design and data analy

  Title: Standard Occupational Classification System (similarity: 0.5135)
  URL: https://en.wikipedia.org/wiki/Standard_Occupational_Classification_System
  QID: Q7598269
  Extract: The Standard Occupational Classification (SOC) System is a United States government system for classifying occupations. It is used by U.S. federal government agencies collecting occupational data, enabling comparison of occupations across data sets. For example, data from the Occupational Requiremen

  Title: Women in the United States (similarity: 0.3509)
  URL: https://en.wikipedia.org/wiki/Women_in_the_United_States
  QID: Q17514041
  Extract: The legal status of women in the United States has advanced significantly over the past two centuries, but not yet equal to that of men in comparison to other high-income democracies.

  Title: Computer engineering (similarity: 0.5098)
  URL: https://en.wikipedia.org/wiki/Computer_engineering
  QID: Q428691
  Extract: Computer engineering is a branch of engineering specialized in developing computer hardware and software.

model_pass1          : claude-sonnet-4-20250514
model_pass2          : claude-haiku-4-5-20251001
inference_confidence : medium
confidence_notes     : This catch-all category presents inherent challenges in creating specific guidance due to its broad scope. Job titles provide some clarity but the absence of detailed O*NET data limits precision in describing work activities and requirements.
inferred_at          : 2026-06-03T14:04:59.894723+00:00
tokens_input         : 2,361
tokens_output        : 3,323
cost_usd             : $0.035339
wikipedia_used       : True
wikipedia_title      : Computer (occupation)
wikipedia_note       : The Wikipedia match refers to historical human computers who performed calculations, which provides interesting context for the evolution of computer-related occupations but has limited relevance to modern technology roles.

--- PROSE FIELDS ---

ROLE SUMMARY:
Computer Occupations, All Other represents a broad catch-all category encompassing specialized technology roles not classified elsewhere in the SOC system. This includes emerging positions like AI specialists, traditional roles like computer operators, and hybrid positions blending technology with business functions. With 435,370 professionals earning a median of $116,580 annually, this category reflects the diverse and evolving nature of the technology workforce.

DAY IN THE LIFE:
Daily responsibilities vary dramatically across this heterogeneous category, spanning from data center technicians monitoring server infrastructure to AI specialists developing machine learning models. E-commerce specialists might optimize online platforms while information systems technicians troubleshoot network issues. Computer laboratory technicians maintain equipment and run diagnostics, while IT consultants analyze business requirements and recommend technology solutions. Content managers oversee digital assets and user experience, demonstrating the broad spectrum of technology-adjacent roles captured in this classification.

WHO THRIVES:
Success in these varied roles typically requires strong analytical thinking and attention to detail, given the technical problem-solving nature of most positions. Individuals who excel tend to be methodical and process-oriented, comfortable working with both technology systems and business stakeholders. The diversity of roles means some positions favor technical depth while others require broad technology literacy combined with communication skills. Continuous learners who adapt to rapidly changing technology landscapes and those who can bridge technical concepts with practical business applications tend to flourish in these positions.

CAREER ENTRY:
Entry pathways vary significantly given the breadth of roles, ranging from technical certificates and associate degrees for operator positions to bachelor's degrees in computer science or related fields for specialist roles. Some positions like data center technician may require industry certifications and hands-on technical training, while consultant roles often demand business experience alongside technical knowledge. The emerging nature of many positions means alternative pathways through bootcamps, online training, or transferring from adjacent fields are increasingly common.

CAREER TRAJECTORY:
Career advancement typically leads toward specialized technical expertise, management roles, or senior consulting positions depending on the starting role and individual interests. Data center technicians might progress to systems administration or infrastructure management, while AI specialists could advance to machine learning engineering or research roles. Many professionals transition into more defined SOC categories as they specialize, moving toward established roles like software development, systems analysis, or information security that offer clearer advancement structures and higher compensation potential.

MARKET INTELLIGENCE:
The category shows robust employment of 435,370 professionals with a strong median wage of $116,580 according to BLS OEWS May 2025, reflecting healthy demand for diverse technology skills. Geographic variation is substantial, with District of Columbia leading at $156,590 versus Puerto Rico at $60,470, indicating concentration in major technology and government centers. Professional services employs the most workers (123,970), followed by government (97,870), suggesting strong demand in both private consulting and public sector technology roles. The wide salary range from $55,940 to $188,470 reflects the category's diversity, from entry-level operator positions to high-value specialist roles. Growth prospects vary by specific role, with emerging areas like AI and e-commerce showing particular strength.

AUTOMATION OUTLOOK:
Automation risk varies dramatically across this diverse category, with routine operator positions facing higher displacement risk while specialized consulting and AI development roles remain largely automation-resistant. The category's emphasis on emerging technologies and complex problem-solving provides some protection against automation for most roles.

--- REASONED EDGES ---
  [career_pathway] Computer Systems Analysts (15-1211.00) — confidence:high
    reasoning: Many IT consultants and systems hardware analysts in this category naturally progress to dedicated systems analyst roles
    data: Computer Systems Hardware Analyst job title
    data: Information Technology Consultant job title
    data: Professional services industry employment
  [task_similarity] Computer Network Support Specialists (15-1231.00) — confidence:high
    reasoning: Information Systems Technicians share core network troubleshooting and support responsibilities
    data: Information Systems Technician job title
    data: Government sector employment
  [career_pathway] Network and Computer Systems Administrators (15-1244.00) — confidence:high
    reasoning: Data Center Technicians and Computer Operators often advance to systems administration roles
    data: Data Center Technician job title
    data: Computer Operator job title
  [skill_overlap] Computer and Information Research Scientists (15-1221.00) — confidence:medium
    reasoning: AI Specialists share research-oriented analytical skills with computer research scientists
    data: Artificial Intelligence Specialist job title
    data: Professional services employment
  [task_similarity] Computer Operators (43-9011.00) — confidence:high
    reasoning: Computer Console Operators and Computer Peripheral Equipment Operators perform similar equipment monitoring tasks
    data: Computer Console Operator job title
    data: Computer Peripheral Equipment Operator job title

--- NORMALIZER SIGNALS ---
  match_keywords   : ['computer specialist', 'IT specialist', 'technology consultant', 'AI specialist', 'data center', 'computer operator', 'information systems', 'e-commerce specialist']
  exclude_keywords : ['software developer', 'programmer', 'database administrator', 'security analyst', 'web developer']
  title_patterns   : ['*Computer*Specialist', '*IT*Specialist', '*Technology*Consultant', '*Information*Systems*', '*Data*Center*']
  common_variations: ['General IT Specialist', 'Computer Lab Technician', 'Information Technology Consultant', 'AI Specialist', 'E-Commerce Specialist', 'Content Manager', 'Data Center Operator', 'Computer Systems Hardware Analyst']

total_terms    : 40
top_words      : ['computers', 'human', 'computer', 'women', 'computing', 'war', 'tables', 'technology', 'world', 'occupations', 'mathematical', 'committee', 'worked', 'calculations', 'project', 'united', 'states', 'category', 'technical', 'harvard']
source_layers  : onet_tasks | onet_dimensions | dwas | wikipedia | inference

TERM                    COUNT     FREQ  DOMINANT SOURCE      SOURCE BREAKDOWN
──────────────────────────────────────────────────────────────────────────────────────────
computers                  50  0.03477  wikipedia            wikipedia:98%  inference:2%
human                      33  0.02295  wikipedia            wikipedia:97%  inference:3%
computer                   25  0.01739  wikipedia            wikipedia:80%  inference:20%
women                      20  0.01391  wikipedia            wikipedia:100%
computing                  15  0.01043  wikipedia            wikipedia:100%
war                        15  0.01043  wikipedia            wikipedia:100%
tables                     14  0.00974  wikipedia            wikipedia:100%
technology                 13  0.00904  inference            inference:92%  wikipedia:8%
world                      11  0.00765  wikipedia            wikipedia:100%
occupations                10  0.00695  wikipedia            wikipedia:80%  inference:20%
mathematical                9  0.00626  wikipedia            wikipedia:100%
committee                   9  0.00626  wikipedia            wikipedia:100%
worked                      8  0.00556  wikipedia            wikipedia:100%
calculations                7  0.00487  wikipedia            wikipedia:86%  inference:14%
project                     7  0.00487  wikipedia            wikipedia:100%
united                      7  0.00487  wikipedia            wikipedia:100%
states                      7  0.00487  wikipedia            wikipedia:100%
category                    7  0.00487  inference            inference:100%
technical                   7  0.00487  inference            inference:100%
harvard                     6  0.00417  wikipedia            wikipedia:100%
machine                     6  0.00417  wikipedia            wikipedia:67%  inference:33%
pearson                     6  0.00417  wikipedia            wikipedia:100%
university                  6  0.00417  wikipedia            wikipedia:100%
humans                      6  0.00417  wikipedia            wikipedia:100%
obsolete                    6  0.00417  wikipedia            wikipedia:100%
term                        5  0.00348  wikipedia            wikipedia:100%
century                     5  0.00348  wikipedia            wikipedia:100%
known                       5  0.00348  wikipedia            wikipedia:100%
became                      5  0.00348  wikipedia            wikipedia:100%
calculating                 5  0.00348  wikipedia            wikipedia:100%
hired                       5  0.00348  wikipedia            wikipedia:100%
computation                 5  0.00348  wikipedia            wikipedia:100%
organized                   5  0.00348  wikipedia            wikipedia:100%
working                     5  0.00348  wikipedia            wikipedia:80%  inference:20%
many                        5  0.00348  wikipedia            wikipedia:60%  inference:40%
research                    5  0.00348  wikipedia            wikipedia:80%  inference:20%
engineering                 5  0.00348  wikipedia            wikipedia:80%  inference:20%
hbc                         5  0.00348  wikipedia            wikipedia:100%
business                    5  0.00348  inference            inference:100%
early                       4  0.00278  wikipedia            wikipedia:100%