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Physicists

Also called Research physicist, Experimental physicist, Theoretical physicist, Computational physicist, Applied physicist

In brief

In May 2025, BLS counted 20,430 US physicists, with median pay of $172,250 a year. Of the BLS counts since 2021, the highest was 21,340, in May 2024, close to the May 2025 count. The earliest figure here is an estimate of 400 from 1900, not measured the same way as today's count. The last full technology era for physicists: Deep learning, GPU acceleration, and ML-augmented data pipelines, 2015 to 2022. ML competence became a differentiating skill for physics job applicants from approximately 2017 onward.

What the sources say

Official outlook

BLS projects employment to grow 7.2% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 63.6% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 36% of work tasks in life, physical, and social science occupations, this job's group, are exposed to automation by AI. Not a forecast of job losses. Goldman Sachs, 2023

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Physicists: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-2012

Chicago

Future History. "Physicists: jobs and pay since 1900." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-2012.

BibTeX

@misc{futurehistory_19_2012, author = {{Future History}}, title = {Physicists: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-2012}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

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1850187519001925195019752000now
Country
2026
Known then as Physicist / Natural Philosopher (transitional era; "physicist" coined by Whewell 1840)
Known then as Physicist (early professional era; university chairs and industrial labs)
Known then as Physicist (Cold War golden age; Manhattan Project prestige; Sputnik funding boom)
Known then as Physicist (contraction and rebound; funding bust 1970s, computing revival 1990s)
Known then as Physicists (BLS SOC 19-2012; includes experimental, theoretical, and applied sub-fields)
Known today as Physicists (BLS SOC 19-2012; includes experimental, theoretical, and applied sub-fields)
Projected to still be called Physicists (BLS SOC 19-2012; includes experimental, theoretical, and applied sub-fields)
Data from the May 2025 survey
US Employment
20K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$172,250

≈ $167,834 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1840

William Whewell, polymath and Master of Trinity College Cambridge, coins the term "physicist" in his 1840 book The Philosophy of the Inductive Sciences, using it to denote a cultivator of physics as distinct from a physician. Whewell also coined "scientist," "ion," "cathode," and "anode" in the same era. The naming crystallizes a professional identity that had been forming for decades in German research universities and the British scientific societies.

Beat · 1899

The American Physical Society is founded on May 20, 1899, at Columbia University with 59 founding members. The formation of the APS marks the institutionalization of physics as a distinct professional community in the United States, parallel to the Deutsche Physikalische Gesellschaft (1845) and the Physical Society of London (1874). The APS will grow to over 50,000 members by the early 21st century and serve as the primary tracker of physicist workforce statistics through its Statistical Research program.

Beat · 1945

The Trinity test on July 16, 1945, and the subsequent detonations at Hiroshima and Nagasaki transform the public image of the physicist from an obscure academic to a figure of world-historical consequence. Robert Oppenheimer's face appears on magazine covers. The Office of Scientific Research and Development, which had funded the wartime physics mobilization, becomes the model for the National Science Foundation (established 1950) and the Atomic Energy Commission. These two agencies will fund the Cold War physics expansion that takes the workforce from 13,000 in 1953 to approximately 47,000 by 1968.

Beat · 1957

October 4, 1957: the Soviet Union launches Sputnik, the first artificial Earth satellite. The US Congress passes the National Defense Education Act in 1958, dramatically expanding funding for physics education and research. Physics Today reports that the AIP's educational activities "increased many fold" in 1957. The AIP Placement Service in 1962 records 514 job openings against 449 applicants -- a 1.1:1 ratio indicating full employment and the beginning of a decade-long seller's market for physicists.

Beat · 1971

The physics job market collapses. The AIP Placement Service records 53 jobs for 1,053 applicants in 1971 -- the ratio has flipped from 1.1:1 in 1962 to 20:1 in a decade. The crisis arrives from multiple directions simultaneously: NASA's post-Apollo budget cuts, the end of the Vietnam War defense build-up, the 1969-1970 recession, and the continued overproduction of physics PhDs (1,500 per year by 1970 against a contracting market). Many PhDs who cannot find physics positions pivot into computing, finance, and other quantitative fields -- beginning the pattern of physics-to-adjacent-industry outflow that continues to the present.

Beat · 2022

February 16, 2022: DeepMind and EPFL publish "Magnetic control of tokamak plasmas through deep reinforcement learning" in Nature (volume 602, pages 414-419). A single neural network reads 90 sensor measurements and adjusts 19 magnetic coil voltages 10,000 times per second, maintaining stable plasma in the TCV tokamak at diverse configurations including the snowflake and droplet shapes. The paper signals a paradigm shift: for the first time, an AI system has replaced the hand-coded control logic at the heart of a physics experiment, trained entirely in simulation and transferred to hardware. Fusion physicists who understand RL and can specify reward functions for plasma shape constraints are immediately more valuable than those who cannot.

Beat · 2023

DeepMind publishes GNoME (Graph Networks for Materials Exploration) in Nature, predicting 2.2 million new crystal candidates with a graph neural network. Over 380,000 are predicted to sit within 1 meV/atom of the convex hull -- the threshold for thermodynamic stability. Within a year, over 700 predicted structures have been independently synthesized in laboratories using AI-assisted robotic synthesis (the A-Lab at Lawrence Berkeley). For materials physicists, the GNoME result collapses the hypothesis space: instead of synthesizing hundreds of candidates to find stable phases, the physicist now synthesizes a short list pre-filtered by the model. The role shifts upstream toward specifying search objectives and interpreting predictions, rather than executing the full synthesis and characterization cycle.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace physicists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+7.2%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-2012 (Physicists): 23.2 thousand jobs in 2025, projected 24.8 thousand in 2035, a change of +1.7 thousand (+7.2%). Matrix employment counts all jobs in the occupation, including self-employed workers (7% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
63.6%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-2012.00 Physicists. Shown: human-rated β = 0.636, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.5 and γ (all LLM-assisted) 0.773; GPT-4-rated β 0.75. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Goldman Sachs (Briggs & Kodnani, 2023), "The Potentially Large Effects of Artificial Intelligence on Economic Growth"
2023 study · Share of work tasks exposed to automation by AI, all life, physical, and social science occupations (US)
36%
of tasks
What this measures

Goldman Sachs Global Investment Research scored the task content of each US occupation for exposure to automation by generative AI and weighted by OEWS employment. Exhibit 5 (US panel) reports 36% for life, physical, and social science occupations, the occupational group this role belongs to; the report publishes no figure for individual occupations. It measures how much of the group's work AI could automate, not how many jobs disappear, and it is a 2023 estimate, not a forecast for a later year.

Official statisticReport an error
Today, in this role

What's changing day to day for physicists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereConduct AI-assisted literature triage and theoretical framework review before proposing a new research direction: using Elicit to query 200M+ papers by research question to surface existing experimental measurements, competing theoretical explanations, and replication status

Conduct AI-assisted literature triage and theoretical framework review before proposing a new research direction: using Elicit to query 200M+ papers by research question to surface existing experimental measurements, competing theoretical explanations, and replication status; using Scite.ai Smart Citations to verify whether key papers in the proposed research area have been supported or disputed by subsequent work; synthesizing findings into a research proposal that situates the proposed work against the current state of knowledge with defensible citation support.[14],[15],[1]

Tools picking this up
Where your edge is

Elicit and Scite compress the pre-proposal literature survey from weeks of journal-by-journal searching to a structured synthesis session: a genuine research productivity multiplier. The physicist must personally read the primary sources for the most consequential claims: AI literature synthesis can misattribute findings, miss preprints on arXiv that have not yet been indexed, or aggregate results from methodologically incompatible experiments without flagging the incompatibility. For any experimental measurement central to your proposal, trace it to the primary paper, read the systematic uncertainty section, and evaluate whether the reported precision is limited by statistics (and therefore improvable) or by systematics (and therefore not easily improvable with more data at the same facility).

AI is sitting alongside you hereRun model-agnostic searches for new physics at the LHC using ML-based anomaly detection, deploying CWoLa (Classification Without Labels) or autoencoder-based algorithms on CMS/ATLAS collision datasets to identify statistically significant deviations from Standard Model predictions without pre-specifying a signal hypothesis

Run model-agnostic searches for new physics at the LHC using ML-based anomaly detection, deploying CWoLa (Classification Without Labels) or autoencoder-based algorithms on CMS/ATLAS collision datasets to identify statistically significant deviations from Standard Model predictions without pre-specifying a signal hypothesis; evaluating false-positive rates and signal-injection efficiency; contributing to the AutoDQM automated data quality monitoring system that flags detector anomalies in real time across ECAL, HCAL, and tracker subsystems during Run 3 operations.[16],[6],[17]

Where your edge is

ML anomaly detection dramatically expands the model-agnostic search space beyond what hypothesis-specific cut-based analyses can cover: a genuine new scientific capability, not merely efficiency. Your irreducible contribution is validating that observed anomalies are physical (not instrumental artifacts or mis-modeled backgrounds): scrutinize the detector acceptance in the anomalous kinematic region, check systematic uncertainties associated with jet energy scale, pileup reweighting, and trigger efficiency, and run the same search on a control sample where the answer is known before claiming a result. CWoLa sensitivity degrades when signal fraction is very low: understand the regime where the method is reliable for your specific channel.

AI is sitting alongside you hereAccelerate condensed matter theory and materials property prediction by using MACE-MP-0 or other universal neural network potentials (NNPs) for molecular dynamics simulations, replacing computationally expensive DFT calculations with an NNP inference pass to run nanosecond-timescale MD simulations across diverse chemical environments

Accelerate condensed matter theory and materials property prediction by using MACE-MP-0 or other universal neural network potentials (NNPs) for molecular dynamics simulations, replacing computationally expensive DFT calculations with an NNP inference pass to run nanosecond-timescale MD simulations across diverse chemical environments; benchmarking NNP energies and forces against DFT reference calculations for the specific material family under study; using GNoME-predicted stable crystal structures as starting geometries for phonon dispersion and electronic structure calculations.[8],[7],[18]

Where your edge is

MACE-MP-0 achieves near-DFT accuracy across most of the periodic table at orders-of-magnitude lower cost than DFT, making previously intractable simulations (nanosecond MD of disordered solids, large supercells, high-temperature configurational sampling) routine. But transferability to extreme conditions matters: NNPs trained on the Materials Project dataset are optimized for ground-state near-equilibrium geometries. For high-pressure phases, highly strained interfaces, defect-rich environments, or reaction pathways that traverse large-barrier transition states, validate against single-point DFT calculations on configurations representative of the most distorted geometries in your simulation trajectory: NNP errors compound in long MD runs if the potential surface is poorly sampled in training.

Where physicists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Data Scientists

Physics PhDs are among the most natural feeders into Data Scientist roles: they arrive with deep probabilistic reasoning, uncertainty quantification fluency, comfort with high-dimensional datasets, and production Python/C++ experience from analysis pipelines. The quantum computing and AI-for-science boom has created a structural premium for data scientists with genuine physics domain knowledge: companies building ML tools for particle physics analysis (Pythia8-ML, ATLAS ML frameworks), materials simulation (Citrine, Rowan), fusion control systems (Commonwealth Fusion Systems, TAE Technologies, Helion), or astrophysics data pipelines (Vera Rubin software consortium, DESI collaboration) actively prefer physics PhDs over pure CS candidates who lack physical intuition for model failure modes. The transition requires adding software engineering best practices and MLOps depth to the statistical and numerical skills already present.

What you'd add
  • · Production ML stack: PyTorch or JAX for deep learning, scikit-learn for classical models, XGBoost for tabular data; model training, validation, and hyperparameter optimization discipline beyond the one-off analysis scripts typical of physics code
  • · Physics-specific ML: graph neural networks for particle physics (PyG / DGL) or molecular property prediction; normalizing flows and generative models for density estimation in high-dimensional phase space; neural network potentials (MACE, NequIP) for materials simulation
  • · MLOps and software engineering: MLflow or W&B for experiment tracking, FastAPI for serving inference models, Docker for reproducible environments, CI/CD for analysis codebases; writing test suites for numerical code
  • · Statistical inference at scale: Bayesian inference with probabilistic programming (PyMC, NumPyro), approximate inference techniques (variational inference, MCMC), calibration and uncertainty quantification for deployed models
  • · Business translation: framing physics-trained probabilistic reasoning in terms of precision-recall trade-offs, false-positive costs, and ROI metrics that non-physics stakeholders and engineering leadership respond to
What it takesSome new skills to pick up
Tools of the era

How has the work of physicists changed since 1840?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Classical instruments: spectroscopes, galvanometers, vacuum tubes, and analytical balance

    The 19th-century physicist worked with instruments built in the laboratory or purchased from specialist instrument-makers: Bunsen burners, prisms, and diffraction gratings for spectroscopy; galvanometers and Wheatstone bridges for electrical measurement; vacuum pumps enabling cathode-ray experiments. Calculation was done by hand, aided from the 1870s by printed logarithm tables and later by mechanical arithmometers. The pace of physics was set by how fast a skilled experimenter could design, build, and operate precision apparatus -- with no electronic amplification, no photography beyond the daguerreotype, and no way to record data except written logs.

  • X-ray and radioactive sources, cloud chambers, cyclotrons

    Roentgen's discovery of X-rays in 1895 and Becquerel's discovery of radioactivity in 1896 opened experimental regimes that reshaped the occupation. The Wilson cloud chamber (1911), the mass spectrograph (Aston, 1919), and the Geiger-Muller tube (1928) gave physicists tools to probe atomic and nuclear structure directly. Ernest Lawrence's first cyclotron (1930) at Berkeley, and the subsequent machines, established the model of big-physics infrastructure that would define the occupation for the rest of the century: specialized accelerator facilities, large teams, and purpose-built detector systems. The Manhattan Project from 1942-1945 compressed a decade of nuclear physics development into three years, employing more than a hundred thousand people across sites -- with physicists as the core technical leadership at Los Alamos.

    Effect on the work

    The Manhattan Project tripled or quadrupled the US physics workforce in three years, then redistributed those physicists into the DOE national laboratory network, universities, and industrial research labs. This wartime mobilization established physics as a profession capable of absorbing large-scale government investment.

  • Electronic computers, analog signal processing, and Cold War accelerator infrastructure

    ENIAC (1945) and the rapid development of scientific computing through the late 1940s and 1950s gave physicists their first electronic calculation tools. Early computers at Los Alamos, Argonne, and Brookhaven National Laboratories were operated by physicists who wrote their own machine-code programs. By the mid-1960s, mainframe computers had become standard equipment at national labs and research universities. IBM 7094 and CDC 6600 systems ran Monte Carlo simulations of nuclear reactions, particle shower cascades, and diffusion problems that had previously required months of manual calculation. Simultaneously, the postwar decade saw the construction of a network of high-energy particle accelerators -- Brookhaven Cosmotron (1952), Berkeley Bevatron (1954), CERN PS (1959) -- each requiring large detector teams and driving specialization within the occupation.

    Effect on the work

    The combination of NSF, NASA, and AEC funding surges after Sputnik (1957) expanded physicist employment from approximately 13,000 in 1953 to an estimated 47,000 by 1968 -- a nearly four-fold increase in fifteen years. The abrupt end of that funding cycle produced the 1970-1972 bust in which the AIP Placement Service recorded many job openings against 1,053 applicants.

  • Minicomputers, FORTRAN workstations, and specialized analysis software (ROOT precursors)

    The minicomputer era -- DEC PDP-11 systems from the early 1970s, followed by VAX workstations in the early 1980s and Unix workstations (Sun, HP) by the late 1980s -- gave individual physicists or small groups direct computing access for the first time. FORTRAN had been the standard language of scientific computation since the late 1950s; now it ran on machines a research group could own rather than a machine the national lab operated in batch mode. Data analysis workflows that had been paper-coded became interactive scripts. By the mid-1980s, physics computing had developed specialized packages: GEANT for particle detector simulation, PAW (Physics Analysis Workstation) for high-energy physics data analysis, MATLAB for numerical computing. The physicist's toolkit expanded from the physical laboratory into a dual environment of bench and workstation.

  • High-throughput computing clusters, Python/NumPy/SciPy stack, and the Large Hadron Collider era

    The late 1990s and 2000s brought commodity Linux clusters that replaced expensive workstation arrays, making teraflop-scale computation accessible to university research groups. Python's scientific ecosystem -- NumPy (2005-2006), SciPy (2001), Matplotlib (2003), and eventually IPython/Jupyter (2001/2014) -- standardized the physicist's computational toolkit across sub-fields. The Large Hadron Collider, which began colliding protons in 2008, generated many petabytes of data per year and drove the development of distributed computing grids (WLCG) that collectively involved tens of thousands of physicists worldwide. For condensed matter and materials physicists, DFT codes (VASP, Quantum ESPRESSO) enabled first-principles calculations of material properties that had previously required experimental synthesis. The occupation split further into computational and experimental tracks that often worked together on the same problem.

    Effect on the work

    The internet and computing boom of the 1990s created a competing demand for physics-trained quantitative workers in finance, software, and data analytics. Many physicists who could not find academic or national-lab positions pivoted into quantitative finance (the "quant" role) or technology companies, making physics PhDs a sought-after commodity in non-physics markets for the first time.

  • Deep learning (TensorFlow/PyTorch), GPU acceleration, and ML-augmented data pipelines

    Deep learning's rapid maturation after 2012 reached physics research with a lag of three to five years. By 2015-2016, particle physicists at CERN had begun deploying neural networks for jet classification and anomaly detection. By 2018, the CWoLa (Classification Without Labels) paradigm had demonstrated model-agnostic searches for new physics at the LHC. Condensed matter physicists began using graph neural networks and variational autoencoders for material property prediction. The DeepMind-EPFL plasma control paper in Nature (February 2022) demonstrated that deep reinforcement learning could replace hand-programmed coil-control logic in a tokamak, reading many sensors and adjusting several coils simultaneously at 10,000 Hz. These applications were not workflow helpers but genuine new scientific capabilities -- search methods and control systems that were impossible with prior tools.

    Effect on the work

    ML competence became a differentiating skill for physics job applicants from approximately 2017 onward. Particle physics collaborations began formally requiring knowledge of PyTorch or TensorFlow for analysis roles. The physicist who had mastered ML pipelines commanded a premium both within physics (for grant success and collaboration leadership) and outside it (for data science and AI research industry positions).

  • Foundation models, neural network potentials, and AI-co-authored physics (GNoME, MACE-MP-0, Claude/GPT for derivations)

    From 2022, the pace of AI tool adoption in physics accelerated beyond deep learning into foundation models and universal neural network potentials. GNoME (DeepMind, 2023) identified 2.2 million crystal candidates. MACE-MP-0 (Cambridge, 2023) trained a single potential covering many elements at near-DFT accuracy, enabling nanosecond molecular dynamics simulations that were previously computationally prohibitive. The Vera C. Rubin Observatory began delivering millions of transient alerts per night from late 2025, requiring ML broker pipelines (Lasair, ANTARES) because manual inspection is physically impossible at that data rate. At the AI-assisted writing layer, the Anthropic "Vibe Physics" case study (2026) demonstrated a high-energy theory paper co-authored by Claude Opus in two weeks versus the usual year -- with the human professor driving the science and verifying every step. The 2026 APS survey of its membership found that AI tools had become a "facet of everyday life" in physics research.

    Effect on the work

    The APS 2026 survey documented widespread use of AI tools for derivation checking, code debugging, and grant proposal drafting. Physicists described AI outputs as "sloppy" and requiring domain-expert review -- framing the occupation's irreplaceable value as the judgment to evaluate what the AI produced, not the production itself.

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Physicists employment and pay by year

On record since1840
Latest US employment20,430 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$172,250 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+7.2% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 21100)

    Employment, 2020
    2,620, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 21100 Physicists and astronomers, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$108,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21100 Physicists and astronomers, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 29 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1900400n/aEst., Physicist Researched contextReport
19403,900n/aEst., FRASER / BLS Bulletin No. 1144 Researched contextReport
195313,000$6,800Est., FRASER / BLS Bulletin No. 1144 Researched contextReport
196847,000n/aEst., Bureau of Labor Statistics / FRASER Researched contextReport
197138,000n/aEst., Physics Today Researched contextReport
20008,990$83,310BLS OEWS 2000 Official statisticReport
200312,390$85,630BLS OEWS May 2003 Official statisticReport
200414,150$87,450BLS OEWS May 2004 Official statisticReport
200515,160$89,810BLS OEWS May 2005 Official statisticReport
200615,420$94,240BLS OEWS May 2006 Official statisticReport
200713,980$96,850BLS OEWS May 2007 Official statisticReport
200814,810$102,890BLS OEWS May 2008 Official statisticReport
200913,630$106,390BLS OEWS May 2009 Official statisticReport
201016,860$106,370BLS OEWS May 2010 Official statisticReport
201116,220$106,360BLS OEWS May 2011 Official statisticReport
201217,820$106,840BLS OEWS May 2012 Official statisticReport
201317,340$110,110BLS OEWS May 2013 Official statisticReport
201416,790$109,600BLS OEWS May 2014 Official statisticReport
201515,650$111,580BLS OEWS May 2015 Official statisticReport
201616,680$115,870BLS OEWS May 2016 Official statisticReport
201716,710$118,830BLS OEWS May 2017 Official statisticReport
201817,620$120,950BLS OEWS May 2018 Official statisticReport
201916,730$122,850BLS OEWS May 2019 Official statisticReport
202016,160$129,850BLS OEWS May 2020 Official statisticReport
202120,020$152,430BLS OEWS May 2021 Official statisticReport
202218,840$142,850BLS OEWS May 2022 Official statisticReport
202318,350$155,680BLS OEWS May 2023 Official statisticReport
202421,340$166,290BLS OEWS May 2024 (employment) Official statisticBLS OEWS May 2024 (pay) Official statisticReport
202520,430$172,250BLS OEWS May 2025 Official statisticReport
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