An in-depth dataset with statistics and insights related to career changes, including frequency, reasons, age-based trends, industry shifts, and psychological drivers for switching careers.

By the last business day of September 2024, there were about 7.44 million job openings in the United States. This is a decrease from the previous month, when there were 7.86 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.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

This dataset was obtained from the Google Jobs API through serpAPI and contains information about job offers for data scientists in companies based in the United States of America (USA). The data may include details such as job title, company name, location, job description, salary range, and other relevant information. The dataset is likely to be valuable for individuals seeking to understand the job market for data scientists in the USA and for companies looking to recruit data scientists. It may also be useful for researchers who are interested in exploring trends and patterns in the job market for data scientists. The data should be used with caution, as the API source may not cover all job offers in the USA and the information provided by the companies may not always be accurate or up-to-date.

🧠 data_jobs Dataset

A dataset of real-world data analytics job postings from 2023, collected and processed by Luke Barousse.

  Background

I've been collecting data on data job postings since 2022. I've been using a bot to scrape the data from Google, which come from a variety of sources. You can find the full dataset at my app datanerd.tech.

Serpapi has kindly supported my work by providing me access to their API. Tell them I sent you and get 20% off paid plans.… See the full description on the dataset page: https://huggingface.co/datasets/lukebarousse/data_jobs.

The Job listings dataset gives access key details about job opportunities, including job titles, company names, locations, and employment specifics. With direct application links and insights into application numbers, this dataset streamlines job searching and market trend analysis.Tailored for job seekers, recruiters, and market analysts, this dataset supports efficient decision-making in the competitive job market. Whether you're tracking hiring trends, optimizing recruitment strategies, or searching for your next career opportunity, the job listings information dataset is an invaluable tool for navigating the job landscape. Available job datasets

Indeed job listings information Glassdoor job listings information LinkedIn job listings information LinkedIn profiles job listings information

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by province and territory, last 5 quarters.

The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

Graph and download economic data for Job Openings: Total Nonfarm (JTSJOR) from Dec 2000 to May 2025 about job openings, vacancy, nonfarm, and USA.

Job Offers in the United States increased to 7769 Thousand in May from 7395 Thousand in April 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.

Current Employment by Industry (CES) data reflect jobs by "place of work." It does not include the self-employed, unpaid family workers, and private household employees. Jobs located in the county or the metropolitan area that pay wages and salaries are counted although workers may live outside the area. Jobs are counted regardless of the number of hours worked. Individuals who hold more than one job (i.e. multiple job holders) may be counted more than once. The employment figure is an estimate of the number of jobs in the area (regardless of the place of residence of the workers) rather than a count of jobs held by the residents of the area.

Data from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

On the last business day of April 2025, there were about **** million job openings in the professional and business services industry in the United States, the highest number of job openings after the private education and health services industries. The mining and logging industry, however, had about ****** job openings during the same month. The data is seasonally adjusted.

Globally, the textile and apparel employs over ********** workers along its value chain, the largest opportunity regarding green employment is in reinventing the textile and apparel value chains. Investment in urban green transportation has the potential to increase jobs, adding ************ new direct jobs between now and 2030.

LinkUp Raw contains the most complete set of LinkUp’s job market data, dating back to 2007. The dataset includes full job postings sourced directly from employer websites as well as statistics and analysis of the job postings. Delivered daily, LinkUp Raw consists of 4 distinct components: individual job records, individual descriptions, core aggregates, and company reference data.

The dataset includes over 20 unique job and company level attributes and identifiers, such as, job title, full job description, job URL, company name, location, occupation code, sector code, ticker, reference data, and more.

Use Cases for Job Market Data:

-Rely on a unique, timely, and accurate job listings dataset sourced directly from employer websites globally. LinkUp collects, refines, and distributes data to deliver insightful, predictive, and actionable job market information.

-Perform deeper analysis with extensive global coverage, irreplaceable history, and over 20 unique job and company level attributes and identifiers.

-Enhance Alpha. Create models and signals to assess and predict job growth at the macro, sector, geographic, and individual company level

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

This chart provides a detailed overview of the number of Jobs & Education online retailers by Monthly Product Sold. Most Jobs & Education stores' Monthly Product Sold are Less than 100, there are 130.25K stores, which is 79.49% of total. In second place, 26.18K stores' Monthly Product Sold are 100 to 1K, which is 15.98% of total. Meanwhile, 4.59K stores' Monthly Product Sold are 1K to 10K, which is 2.80% of total. This breakdown reveals insights into Jobs & Education stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.