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Data scientists

Also called DS, ML scientist, Applied scientist, Senior data scientist, Staff data scientist

In brief

In May 2025, BLS counted 262,440 US data scientists, with median pay of $120,230 a year. Of the BLS counts since 2021, the May 2025 count is the highest. The earliest figure here is an estimate of 50,000 from 2014, not measured the same way as today's count. The last full technology era for data scientists: MLOps + AutoML + feature stores, 2019 to 2022.

What the sources say

Official outlook

BLS projects employment to grow 34.6% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

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). Data scientists: jobs and pay since 2014 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/15-2051

Chicago

Future History. "Data scientists: jobs and pay since 2014." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/15-2051.

BibTeX

@misc{futurehistory_15_2051, author = {{Future History}}, title = {Data scientists: jobs and pay since 2014}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/15-2051}}, 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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19752000now
Country
2026
Known then as Statistician / Analyst (academic and industrial predecessor)
Known then as Quantitative Analyst / Statistical Analyst / "Data Scientist" (Naur's academic usage)
Known then as Data Analyst / Quantitative Scientist / Applied Statistician (pre-big-data industry era)
Known then as Data Scientist (industry title, pre-BLS codification)
Known then as Data Scientists (BLS SOC 15-2051, 2018 SOC revision)
Known today as Data Scientists (BLS SOC 15-2051, 2018 SOC revision)
Projected to still be called Data Scientists (BLS SOC 15-2051, 2018 SOC revision)
Data from the May 2025 survey
US Employment
262K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$120,230
Est.

≈ $117,148 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1962

John W. Tukey publishes "The Future of Data Analysis" in the Annals of Mathematical Statistics. In 67 pages, he argues that data analysis deserves recognition as a scientific discipline separate from classical statistics, distinct from pure probability theory, and oriented toward practical insight rather than mathematical elegance. He identifies himself as a data analyst rather than a statistician. The paper is later identified by statistician David Hand as the founding document of what became data science.

Beat · 1974

Danish computer scientist Peter Naur publishes "Concise Survey of Computer Methods," in which he proposes "data science" as a preferable alternative name for computer science, defined as "the science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences." This is the first known use of "data science" as a term in print, 34 years before the job title would be coined in Silicon Valley.

Beat · 2008

DJ Patil at LinkedIn and Jeff Hammerbacher at Facebook independently reach for the same job title, "data scientist," to describe professionals who combine statistical skill, programming fluency, and domain curiosity to extract insight from very large datasets. Neither existing title (statistician, analyst, programmer) captured the hybrid nature of the work. The term is settled on in early 2008. Within months, it appears in job listings across Silicon Valley technology companies.

Beat · 2011

McKinsey Global Institute publishes "Big Data: The Next Frontier for Innovation, Competition, and Productivity" (May 2011), projecting a US shortage of 140,000 to 190,000 people with "deep analytical talent" and 1.5 million data-savvy managers needed to take advantage of big data. The report is the first major institutional document to quantify the labour shortage in the emerging field, and it catalyses a wave of corporate data science team formation and academic programme creation.

Beat · 2012

Harvard Business Review publishes "Data Scientist: The Sexiest Job of the 21st Century" by Thomas H. Davenport and DJ Patil in its October 2012 issue. The cover article declares data scientists to be among the rarest and most valuable people in the economy, combining statistical, computational, and communication skills in a way that few professionals then did. The piece becomes one of the most cited business articles of the decade, triggering a wave of data science degree programmes, bootcamps, and corporate talent initiatives.

Beat · 2018

The 2018 revision of the Standard Occupational Classification creates SOC code 15-2051, "Data Scientists," as a standalone occupation for the first time. Prior to 2018, data scientists were distributed across several existing codes: 15-2041 (Statisticians), 15-2031 (Operations Research Analysts), 15-1221 (Computer and Information Research Scientists), and various 15-1xxx software roles. The 2018 SOC revision is the official institutional recognition that data science had become a distinct profession, 10 years after Patil and Hammerbacher coined the title.

Beat · 2022

OpenAI releases ChatGPT publicly on November 30, 2022. Within weeks, data scientists begin integrating LLM-based tools into their workflows: generating boilerplate code, synthesising literature, and querying their datasets in natural language. The immediate employment effect is the opposite of the displacement some predicted: demand for data scientists to build, evaluate, govern, and improve LLM-based systems surges. The BLS Occupational Outlook Handbook count for data scientists rises from 168,900 in 2022 to 245,900 in 2024, an increase of more than 45% in two years.

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 data scientists? 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
+34.6%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 15-2051 (Data scientists): 275.6 thousand jobs in 2025, projected 371.0 thousand in 2035, a change of +95.4 thousand (+34.6%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.9% 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)
59.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 15-2051.00 Data Scientists. Shown: human-rated β = 0.594, 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) 0.25 and γ (all LLM-assisted) 0.938; 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
Today, in this role

What's changing day to day for data scientists

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

AI is sitting alongside you hereRun natural-language queries against cloud data warehouses using Cortex AISQL or Snowflake Cortex Analyst, then validate AI-generated SQL for correctness, index efficiency, and schema compliance before sharing results.

Run natural-language queries against cloud data warehouses using Cortex AISQL or Snowflake Cortex Analyst, then validate AI-generated SQL for correctness, index efficiency, and schema compliance before sharing results.[8],[9]

Where your edge is

Cortex AISQL (GA November 2025) and similar tools can generate complex SQL from plain English. The human job shifts to specifying the business question precisely and auditing the output, not writing SQL from scratch.

AI is sitting alongside you hereRun exploratory data analysis (EDA) in AI-assisted notebooks: use Hex Magic or Databricks Genie Code to generate multi-cell SQL+Python analysis from natural-language prompts, then interpret the results and direct follow-up hypotheses.

Run exploratory data analysis (EDA) in AI-assisted notebooks: use Hex Magic or Databricks Genie Code to generate multi-cell SQL+Python analysis from natural-language prompts, then interpret the results and direct follow-up hypotheses.[10],[11]

Where your edge is

Shift time from writing boilerplate EDA code to evaluating AI-generated outputs, catching schema errors, and translating patterns into business hypotheses. Notebook fluency with AI co-pilots is now table-stakes.

AI is sitting alongside you hereBuild, tune, and compare predictive and generative models: select architectures, design feature-engineering pipelines, run hyperparameter sweeps, and evaluate against holdout sets using loss functions and business-relevant metrics.

Build, tune, and compare predictive and generative models: select architectures, design feature-engineering pipelines, run hyperparameter sweeps, and evaluate against holdout sets using loss functions and business-relevant metrics.[4],[3]

Where your edge is

Use AutoML to benchmark baselines quickly; reserve human effort for custom architectures, domain-specific feature engineering, and the judgment calls AutoML cannot make (e.g., bias audits, business-constraint integration).

Where data scientists go next

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

What each move takes

The AI Research Scientist path deepens theoretical foundations (deep learning, reinforcement learning, mathematical optimization) and moves into published-research or R&D environments at labs, academia, or corporate AI divisions. This pivot requires stronger academic credentials (typically a PhD) but offers the highest long-term resilience as AI reshapes applied roles.

What you'd add
· Deep learning theory: transformers, diffusion models, RL from human feedback (RLHF)
What it takesA real upskill, but a natural one
Tools of the era

How has the work of data scientists changed since 1962?

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

  • Mainframe statistical packages (FORTRAN, SPSS 1968, SAS 1976)

    The predecessor practitioners: statisticians, operations research analysts, and quantitative economists relied on mainframe batch computing to run regressions, ANOVA, and time-series analyses. Tukey's "Exploratory Data Analysis" was largely pencil-and-paper work, supplemented by mainframe runs that could take hours to return results. SPSS (Statistical Package for the Social Sciences) was released in 1968 by Norman Nie, Dale Bent, and Hadlai Hull at Stanford, making multivariate statistics accessible to researchers without programming skills. SAS followed in 1976 from North Carolina State University. These tools shaped a generation of analysts who treated data analysis as a slow, deliberate, batch-oriented craft.

    Effect on the work

    The mainframe era created a small, highly credentialed analyst class, primarily in academia, government agencies, and large manufacturers. Estimates of US statisticians and quantitative analysts in the 1970s run to tens of thousands of professionals. The computing barrier kept the field elite and the headcount low.

  • Personal workstation + S/R + early relational databases (SQL, S-Plus, R 1993)

    The arrival of affordable Unix workstations and personal computers transformed statistical computing from a shared-mainframe activity into individual, interactive work. S (developed at Bell Labs by John Chambers beginning in 1975, commercialised as S-Plus in 1988) enabled statistical programming at the command line. R, the open-source implementation of S, was first released by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993 and became the dominant academic statistics language by the early 2000s. SQL relational databases gave analysts direct access to business transaction data for the first time. The combination of R/S-Plus for analysis and SQL for data access defined the workflow of what would become the data scientist predecessor role.

    Effect on the work

    R and SQL democratised quantitative analysis in corporate settings. Analyst headcounts in finance (credit risk, quant trading), insurance (actuarial), and market research grew substantially through the 1990s. BLS tracked statisticians and operations research analysts separately.

  • Python + MapReduce / Hadoop (big data era: Python 2.4, Hadoop 2006, scikit-learn 2007)

    The mid-2000s brought two developments that reshaped the analyst role into what would be called data science. First, Python gained its scientific stack: NumPy (2005), SciPy (2006), and the beginnings of scikit-learn (2007) made Python a credible alternative to R for machine learning. Second, Google's 2004 MapReduce paper and Doug Cutting's Apache Hadoop (2006) gave analysts a framework for processing datasets too large for a single machine. Facebook, Google, LinkedIn, and Amazon were accumulating user-behaviour datasets of a scale no existing statistical toolkit had been designed to handle. The combination of Python fluency, machine learning libraries, and distributed computing was exactly the triangular skillset Patil and Hammerbacher were reaching for when they coined "data scientist" in 2008.

    Effect on the work

    The Hadoop ecosystem and Python ML stack created a new type of practitioner who was neither a pure statistician nor a pure software engineer. LinkedIn data showed that the number of professionals adding "Data Scientist" to their profiles between 2012 and 2015 grew at a rate consistently higher than for software engineers and data analysts.

  • Deep learning + cloud ML platforms (TensorFlow 2015, AWS SageMaker 2017, Jupyter notebooks)

    AlexNet's win at the 2012 ImageNet competition announced deep learning's arrival as a practical engineering tool, not just an academic curiosity. Within three years, TensorFlow (Google, 2015) and PyTorch (Facebook, 2016) gave data scientists production-grade deep learning frameworks. AWS SageMaker (2017), Azure ML, and Google AI Platform began abstracting away the infrastructure layer, letting data scientists focus on model design rather than server management. The Jupyter notebook, originally IPython (Fernando Perez, UC Berkeley, 2001), became the universal data science workspace: a live computational document that combined code, output, and narrative in a single shareable file. This era produced the first formalised data-science education infrastructure: fast.ai (2016), Coursera machine learning specialisations, and dozens of university master's programmes.

    Effect on the work

    The cloud ML era dramatically lowered the barrier to data science practice, expanding the practitioner base far beyond the original Python-Hadoop specialists. The 2018 LinkedIn Workforce Report reported a national skills shortage of more than a hundred thousand data science positions.

  • MLOps + AutoML + feature stores (MLflow 2018, DataBricks Delta Lake 2019, DVC)

    The model-deployment gap became the defining problem of this era. Studies found that most machine learning models never made it to production (VentureBeat, 2019). MLflow (Databricks, 2018), DVC, Weights and Biases, and eventually AWS SageMaker Pipelines addressed the lifecycle gap between building a model in a Jupyter notebook and running it reliably in production. AutoML platforms (Google AutoML, H2O.ai, DataRobot) began automating the model-selection and hyperparameter-tuning work that had previously occupied junior data scientists. The data scientist role bifurcated: routine model training was increasingly automated; senior practitioners concentrated on problem framing, feature engineering, and the production-readiness work that AutoML could not yet handle.

    Effect on the work

    The AutoML era sparked the first serious debate about whether "data scientists will be automated away." In practice, employment continued to grow, because each wave of automation expanded what was computationally possible and thus expanded the scope of problems organisations could tackle, generating more demand for the humans who could direct the automation.

  • LLM-powered tools + agentic notebooks (ChatGPT Nov 2022, GitHub Copilot, Hex Magic, Databricks Genie Code)

    The release of ChatGPT in November 2022 changed the data scientist's workbench within months. GitHub Copilot (general availability June 2022) began generating Python and SQL cells in Jupyter notebooks. Hex Magic (2023) and Databricks Genie Code (2025) went further, translating natural-language data questions directly into multi-cell analytical workflows. The practical effect was to accelerate the routine EDA and boilerplate-code layer of the job significantly. Senior practitioners reported spending less time writing standard cleaning and transformation code and more time on hypothesis formation, model evaluation, stakeholder communication, and the architectural decisions that AI tools could not yet make.

    Effect on the work

    Rather than contracting the field, the LLM era expanded it. Job postings requiring LLM and GenAI skills in data science grew from near-zero in 2022 to a sizeable share of data science listings by 2026 (Medium/AI Analytics Diaries survey of hundreds of postings). GenAI skills commanded a wage premium over baseline data science compensation. BLS projects 33.5% employment growth in 15-2051 from 2024 to 2034.

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Data Scientists employment and pay by year

On record since1962
Latest US employment262,440 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$120,230 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+34.6% 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 21211)

    Employment, 2020
    14,155, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 21211 Data scientists, 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$95,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21211 Data scientists, 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 8 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
201450,000n/aEst., KDnuggets (Gregory Piatetsky-Shapiro) Researched contextReport
2015n/a$91,000Est., Burtch Works Study Researched contextReport
2018100,000n/aEst., KDnuggets (Gregory Piatetsky-Shapiro) Researched contextReport
2021105,980$100,910BLS OEWS May 2021 Official statisticReport
2022159,630$103,500BLS OEWS May 2022 (employment) Official statisticBLS OEWS May 2022 (pay) Official statisticReport
2023192,710$108,020BLS OEWS May 2023 Official statisticReport
2024233,440$112,590BLS OEWS May 2024 Official statisticReport
2025262,440$120,230BLS OEWS May 2025 Official statisticReport
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