Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

Job Growth Statistics: Statistics on job growth are essential in understanding the state and trajectory of an economy because they offer insight into the shifting dynamics of labor markets. By measuring net job addition or subtraction over a certain timeframe, employment growth statistics allow policymakers, companies, and individuals to make well-informed decisions regarding workforce planning, investment decisions, or career choices. Statistics on job growth provide a key measure of economic development as they show whether an economy is expanding, contracting, or remaining stable. Positive employment growth numbers often signal healthy economies with increased consumer spending and company confidence. Conversely, negative or stagnant job growth indicates a slowdown or recession. Furthermore, statistics on employment growth may also be used to highlight developing markets and professions for policymakers as well as job seekers in finding prospective development areas. As such, employment data provides an essential means of measuring an economy's current state and future direction, as well as helping shape policies and initiatives within it. Editor’s Choice From 2020-2030; job growth in the US is anticipated to be 5.3%. Nurse practitioners are predicted to experience the highest job growth; between 2021-2031 at 45.7%; 2019 alone saw sectors producing goods create 188,000 new jobs. Leisure and hospitality job creation decreased by 47% year-on-year between April 2020 and March 2021. President Clinton created 19 million new employment opportunities between June and July of 2022 and 528,000 nonfarm payroll employees were gained; yet by April 2020 20.5 million jobs had been lost from the economy as a whole. By 2031, it is projected that employment opportunities across the nation will reach 166.5 million; over that same timeframe childcare service workers have seen their ranks decline by 336,000. Since the COVID-19 outbreak, healthcare employment levels have suffered a dramatic decrease. By some accounts, over one and a half million employees may have left healthcare jobs since 2016. (Source: zippia.com)

In 2022, half of the young Poles surveyed were looking for a job on the labor market that matches their education profile. However, nearly ** percent of surveyed were looking for a job with the possibility of employment under a contract.

This dataset provides long-term occupational employment projections for the state of California across various industries. It offers insights into job growth, industry trends, and workforce demand over a 10-year horizon.

Why is this dataset useful? 1. Job Market Analysis – Identify which jobs and industries are expected to grow or decline. Workforce Planning – Helps businesses, policymakers, and educators align training programs with future job demand. 2. Predictive Modeling – Use this dataset for time-series forecasting, job demand predictions, and labor market analytics.

Data Details: - Timeframe: 2022-2032 - Geography: State of California - Industries Covered: Technology, Healthcare, Retail, Manufacturing, Finance, and more.

Columns: 1. Area Type – Specifies the geographic classification (e.g., state-level or regional). 2. Area Name – The name of the geographic region (e.g., California, specific labor market regions). 3. Period – The timeframe of the projection, typically from the base year to the projected year (e.g., 2022-2032). 4. SOC Level – The level of the Standard Occupational Classification (SOC) system used for job categorization. 5. Standard Occupational Classification (SOC) – A unique code representing a specific occupation based on the SOC system. 6. Occupational Title – The official job title corresponding to the SOC code. 7. Base Year Employment Estimate – The estimated number of jobs for the occupation in the base year (e.g., 2022). 8. Projected Year Employment Estimate – The expected number of jobs for the occupation in the projected year (e.g., 2032). 9. Numeric Change – The absolute difference in employment between the base year and projected year. 10. Percentage Change – The percentage increase or decrease in employment over the projection period. 11. Exits – Estimated number of workers leaving the occupation due to retirement or career changes. 12. Transfers – Estimated number of workers transferring into or out of an occupation. 13. Total Job Openings – The sum of exits, transfers, and new job creation, representing the total expected openings. 14. Median Hourly Wage – The median hourly wage for the occupation. 15. Median Annual Wage – The median annual wage for the occupation. 16. Entry Level Education – The typical minimum education required for the occupation (e.g., high school diploma, bachelor's degree). 17. Work Experience – The amount of prior work experience typically needed for the occupation. 18. Job Training – The type of on-the-job training required for entry into the occupation.

Potential Use Cases: ✔ Career Guidance – Helps individuals choose high-growth career paths. ✔ Economic Research – Understand how employment trends impact the economy. ✔ Machine Learning Models – Build predictive models for workforce demand.

If you find this dataset useful, please upvote! Your support encourages more high-quality datasets.

Global: ESG Related Job Trends in the Insurance Sector (April 2022 - July 2022)

By the last business day of May 2025, there were about 7.77 million job openings in the United States. This is an increase from the previous month, when there were 7.44 million job openings. The data are seasonally adjusted. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends.

Creating datasets like this takes significant time and effort. If you found this dataset useful, a kind upvote would be greatly appreciated!!

This dataset provides a 30 year comprehensive view of global employment, unemployment, and GDP trends from 1991 to 2022. It includes data of approx 183 countries on employment distribution across agriculture, industry, and services sectors, alongside unemployment rates and GDP figures.

What You Can Do with This Dataset: This dataset opens up several possibilities for analysis and exploration. You can study long-term trends in employment, unemployment, and GDP across countries and regions, and visualize how labor distribution has shifted from agriculture to services over the years. It also allows you to examine the impact of major global events, such as the 2008 Financial Crisis and the 2020 COVID-19 pandemic, on economic and employment patterns. Furthermore, the dataset can be used for time-series forecasting and predictive modeling, helping to estimate future employment trends and GDP growth.

Description of columns:

Country Name – The name of the country.

Year – The year of observation (1991–2022).

Employment Sector: Agriculture – Percentage of total employment in agriculture.

Employment Sector: Industry – Percentage of total employment in industry.

Employment Sector: Services – Percentage of total employment in services.

Unemployment Rate – Percentage of the labor force that is unemployed.

GDP (in USD) – Gross Domestic Product of the country (in U.S. dollars).

In the first quarter of 2025, the employment index in Switzerland was at **** points. This means an increase in employment is expected. However, the index has decreased compared to 2022, when it was at almost ** points. According to the source, the index enables an early assessment of the labor market situation and has a lead time of one quarter compared to the previous year's rate of change. It is based on a survey of more than ***** companies from nine sectors, covering a total of ** percent of private employment. The calculation is based on the average value of the balances of each survey on current employment and planned employment. This is used to determine a weighted average, which produces the indicator.

Job Offers in the United States increased to 7227 Thousand in August from 7208 Thousand in July of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Software Development Job Postings on Indeed in the United States (IHLIDXUSTPSOFTDEVE) from 2020-02-01 to 2025-11-21 about software, jobs, and USA.

EN The Job advertisements are in text formats and in their original language. The SUF data files are available in English in SPSS, Stata and R data formats.

DE Die Stellenanzeigen liegen in Textformaten vor und sind in der jeweiligen Originalsprache abgedruckt. Die SUF-Datendateien liegen in Englisch im SPSS-, Stata- und RData Format vor.

FR Les offres d'emploi sont dans un format texte et dans leur langue d'origine. Les fichiers de données SUF sont disponibles en anglais pour SPSS, Stata et R.

In August 2025, the number of job losers and persons who completed temporary jobs in the United States stood at about 3.4 million and is used when analyzing non-seasonal trends. The monthly unemployment rate can be found here.

Abstract

The Sudan Labor Market Panel Survey 2022 (SLMPS 2022) is the first wave of a planned longitudinal study of the Sudanese labor market designed to elucidate the way in which human resources are developed and deployed in the Sudanese economy. The SLMPS 2022 is a nationally-representative household survey on a panel of about 5,000 households planned to be repeated every six years. The focus of the survey is to understand key relationships between labor market processes and outcomes and other socio-economic processes such as education, training, family formation and fertility, internal and international migration, gender equality and women's empowerment, enterprise development, housing acquisition, and equality of opportunity and intergenerational mobility.

The SLMPS 2022 is modeled on similar surveys carried out in Egypt in 1998, 2006, 2012, and 2018 in Jordan in 2010 and 2016, and in Tunisia in 2014. All of these surveys started out with a sample of 5,000 households in the first wave and then the sample grew as a results of household splits and the addition of a refresher sample in every new wave. The SLMPS 2022 also includes modules from the Living Standards Measurement Study Plus (LSMS+) surveys that focus on gender disaggregated asset, employment, and entrepreneurship data. Given the level of detail desired in the individual level information, it is crucial in this survey that the information be collected from the individual him or herself rather than from any informant in the household. Therefore, the survey design calls for a number of visits to the same household to make sure that each individual aged five and older can be interviewed in person.

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For details on the key characteristics of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Geographic coverage

The sample was designed to provide estimates of the indicators at the national level, for urban and rural areas, and for all regions.

For detailed information on the regions and governorates used in the SLMPS 2022 Sample, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Analysis unit

1- Households. 2- Individuals. 3- Household Enterprises.

Universe

The survey covered a national sample of households and all household's members aged five and above. In addition, the survey covered enterprises operated by the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A fundamental challenge when designing the SLMPS sample was the lack of a recent, nationally representative sample frame. The last national population census in Sudan was in 2008, before the secession of South Sudan. There had also been limited updating of administrative borders and maps. The first level of administrative geography in Sudan is the state (wilaya), and there are 18 states in Sudan. The second level of administrative geography in Sudan is the locality (mahaliya), and CBS had updated the borders of localities in 2017 to 189 distinct geographies (each locality nested within a single state).). The principal investigators (C. Krafft and R. Assaad) used the updated borders combined with 2020 population estimates based on remote sensing data to create our sampling frame and draw our sample. These sources were supplemented with additional data to identify refugee and IDP camps and areas for our strata. The planned sample design was a random stratified cluster sample made up of 5,000 households sub-divided into 250 primary sampling units (PSUs). The strata represented in the sample are: (i) refugee camps, (ii) refugee areas (areas with non-camp refugee settlements), (iii) IDP camps, (iv) IDP areas (areas with non-camp IDP settlements), (v) other (non-refugee/non-IDP) rural areas,

(vi) other urban areas.

For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Sampling deviation

The realities of the sample frame and the logistics of fielding led to a number of deviations from the planned sample in fielding. While the initial sample was estimated to have a reasonable number of households in each PSU based on satellite imaging and population projections, there were cases where a PSU did not, in fact, have any or many households. All PSU locations were reviewed first in the CBS offices to identify locations that were empty or where there appeared to be five or fewer households and these locations were replaced with backup PSUs. There were a variety of reasons why a PSU might have few or no households, including that it consisted of industrial/commercial (not residential) buildings, that it was a mine or grain storage area, or that it had rocks or grain silos that looked like residences. When office review determined there were at least five or more potential households on the satellite maps, fielding was attempted. However, a number of issues arose in the field as well. Upon visiting, buildings were determined to be non-residential, or were abandoned. Furthermore, a number of locations were determined to be unsafe to field, a status that even changed and fluctuated frequently during the fieldwork. Persistent sandstorms also prevented fielding in specific localities. The rainy season likewise made some locations inaccessible for fielding. Backup samples were created; initially one urban and one rural backup were provided per state, and further backups were drawn as needed to replace PSUs that could not be fielded. Backups were, if possible, from the same strata and always from the same state. When possible, additional backups were also drawn from the same locality in an attempt to minimize bias. However, there were cases when an entire locality became inaccessible. Ultimately, 152 PSUs from the original sample of 250 were fielded in the initially planned locations. Nine of the initially planned backups were used. For the remainder, 24 were replaced by the first replacement given, 17 by the second, 17 by the third, 9 by the fourth, 6 by the fifth, 4 by the seventh, and the remaining 12 by various higher order replacements. Repeated replacements tended to occur in localities with a high share of buildings (e.g. mines, grain storage) that the population estimates likely mistook for residences.

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For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Mode of data collection

Face-to-face [f2f]

Research instrument

The SLMPS questionnaires consist of a household questionnaire and an individual questionnaire, with modules. The modules built on and ensured substantial comparability with other LMPSs. The household questionnaire includes: (i) identifiers and household location (ii) roster of household members (iii) housing conditions and durable assets (iv) current household member migrants abroad (v) remittances (vi) other income and transfers (vii) shocks and coping mechanisms (viii) non-agricultural enterprises, including information on characteristics, employment of household members and others, assets, expenditures, and revenue (ix) agricultural assets, land and parcels, capital equipment, livestock, crops, and other agricultural income. The individual questionnaire collects data from all individuals 5 and older (children under five are captured in the household roster). The individual questionnaire elicits information about (i) residential mobility (ii) father's, mother's and sibling characteristics (including siblings abroad) (iv) health (v) education level and detailed educational history (vi) training experiences (vii) skills (viii) current employment and unemployment (viii) job characteristics for the primary and secondary job (ix) labor market history (x) costs and characteristics of marriage (ix) fertility (xii) women's employment (xiii) wages from primary and any secondary jobs (xiv) return migration, refugee, and IDP experiences for Sudanese respondents (xv) modules for immigration and refugees for non-Sudanese respondents (xvi) information technology (xvi) savings and borrowing (xvii) attitudes (xviii) time use (a full 24 hour diary for adults and a shorter module for children) and (xix) a series of questions on rights to parcels, livestock, and durables.

For more details, see the questionnaires in the documentation.

Response

The size of the US labor force declined by 2.3 million people between December 2019 and December 2021. Our experts examine demographic changes to determine if this decline is a passing trend or if it’s here to stay.

Workers in the highest-quality jobs are more likely to remain in those jobs and less likely to be unemployed or leave the labor force. The opposite is true for workers in the lowest-quality jobs. This analysis adds to a growing body of research about job quality and shows that it is an important dimension of the labor market to consider.

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

"The ZipRecruiter Job Seeker Confidence Survey is a nationally representative monthly survey of U.S. job seekers that measures how optimistic or pessimistic they are about their ability to land their preferred jobs. Increased confidence is typically an indicator of future increases in employee turnover, wage growth, and labor force participation."

https://www.ziprecruiter.com/job-seeker-confidence

Q2 2022 United States Job Market Report https://www.joblist.com/jobs-reports/q2-2022-united-states-job-market-report

Image by Dreamstime https://www.dreamstime.com/stock-illustration-folder-index-inscription-job-seekers-d-written-green-register-background-white-pc-keypad-closeup-view-blurred-image88627967

Linkedin was the leading job-related platform in Poland in August 2022, with almost 5.5 million users (website and app). The Pracuj platform followed, with 4.21 million users.

Development of LinkedIn in Poland

LinkedIn is a social media platform mainly used for professional networking and career development. The platform, founded in 2003, allows job seekers to post their resumes and employers to post job opportunities. Half of LinkedIn's Polish users in 2023 were between the ages of 25 and 34. As the popularity of this platform grew, revenue also trended upward, reaching more than 69 thousand U.S. dollars in February 2023.

Poland's labor market

As of early 2023, Poland's unemployment rate stood at 5.9 percent. That rate fell by 0.9 percentage points in July of the same year. With the development of technology, more and more people are looking for jobs online. In 2022, more than 1.1 million job offers were posted on the Polish job search platform Pracuj.pl. There, offers in the retail and IT industries prevailed. The reason why more than half of the Poles were looking for a new job was the expectation of higher wages.

This dataset contains a collection of software engineering job listings scraped from LinkedIn. It provides valuable insights into the current job market, job requirements, and company hiring trends.

If you find this dataset useful, don't forget to hit the upvote button! 😊💝

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Interesting Task Ideas:

1. Analyze the most in-demand software engineering job titles.
2. Explore the geographical distribution of software engineering job opportunities.
3. Identify the most sought-after programming languages and skills in job descriptions.
4. Determine the average years of experience required for different job levels.

Photo by Hack Capital on Unsplash

In 2025, it was estimated that over 163 million Americans were in some form of employment, while 4.16 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.