Explore the "CareerBuilder US Jobs Dataset – August 2021," a valuable resource for understanding the dynamics of the American job market.

This dataset features detailed job listings from CareerBuilder, one of the largest employment websites in the United States, and provides a comprehensive snapshot of job postings as of August 2021.

Key Features:

• Extensive Job Listings: Includes thousands of job postings across various industries and sectors, providing a broad view of employment opportunities in the US.
• Detailed Information: Each listing contains essential details such as job titles, company names, locations, job descriptions, employment types (full-time, part-time, contract), required qualifications, and salary ranges.
• Insights into Trends: Analyze trends in employment, including the most in-demand skills, top hiring companies, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research: This dataset is perfect for researchers, HR professionals, and data analysts interested in understanding the current state of the job market, developing recruitment strategies, or studying labor market dynamics.

By leveraging this dataset, you can gain valuable insights into the US job market as of August 2021, helping you stay ahead of industry trends and make informed decisions. Whether you're a job seeker, employer, or researcher, the CareerBuilder US Jobs Dataset offers a wealth of information to explore.

Employment by Industry reports the total Number of Employers, the Annual Average Employment, and the Annual Average Wage by industry at the town, county, and state level. Industries included in this dataset vary from location to location. In as many locations as possible, five specific industry segments are consistently present (Construction, Manufacturing, Retail Trade, All Industries, Total Government) as well as the largest 3 out of the remaining segments for that location, ranked by Annual Average Employment. Not every location has data for every segment, and some may not have data for the five consistently reported segments. This data is from the Connecticut Department of Labor Quarterly Census of Employment and Wages (QCEW). The program produces a comprehensive tabulation of employment and wage information for workers covered by Connecticut Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program.

Number of employees by North American Industry Classification System (NAICS) and type of employee, last 5 years.

Explore the "Naukri India Jobs Dataset" available on Crawl Feeds, a comprehensive resource offering detailed insights into the Indian job market.

This dataset is derived from job listings on Naukri.com, one of India’s leading job portals, and provides extensive information on job opportunities across various industries and regions.

Key Features:

• Extensive Job Listings: Includes a wide array of job postings from multiple sectors, giving a broad overview of employment opportunities throughout India.
• Detailed Information: Each job listing contains essential details such as job titles, company names, job descriptions, locations, employment types (full-time, part-time, contract), required qualifications, and salary data.
• Insights into Market Trends: Analyze the latest trends in the Indian job market, including high-demand skills, top hiring companies, popular job roles, and geographic distribution of job openings.
• Ideal for Research and Analysis: This dataset is ideal for researchers, HR professionals, and data analysts who are interested in understanding the dynamics of the Indian labor market, developing recruitment strategies, or studying employment trends in India.

The Naukri India Jobs Dataset on Crawl Feeds provides valuable insights into the Indian employment landscape, helping job seekers, employers, and researchers make informed decisions. Utilize this dataset to stay updated on market trends and explore the diverse job opportunities available across India.

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

  <li

AI Job Market & Salary Analysis 2025 Dataset

Dataset Overview

This comprehensive dataset contains detailed information about AI and machine learning job positions, salaries, and market trends across different countries, experience levels, and company sizes. Perfect for data science enthusiasts, career researchers, and market analysts for practice purposes.

Dataset Title

Global AI Job Market & Salary Trends 2025: Complete Analysis of 15,000+ Positions

Dataset Description

What's Inside

It includes detailed salary information, job requirements, company insights, and geographic trends.

Key Features: - 15,000+ job listings from 50+ countries - Salary data in multiple currencies (normalized to USD) - Experience level categorization (Entry, Mid, Senior, Executive) - Company size impact analysis - Remote work trends and patterns - Skills demand analysis - Geographic salary variations - Time-series data showing market evolution

Columns Description

Column	Description	Type
job_id	Unique identifier for each job posting	String
job_title	Standardized job title	String
salary_usd	Annual salary in USD	Integer
salary_currency	Original salary currency	String
salary_local	Salary in local currency	Float
experience_level	EN (Entry), MI (Mid), SE (Senior), EX (Executive)	String
employment_type	FT (Full-time), PT (Part-time), CT (Contract), FL (Freelance)	String
job_category	ML Engineer, Data Scientist, AI Researcher, etc.	String
company_location	Country where company is located	String
company_size	S (Small <50), M (Medium 50-250), L (Large >250)	String
employee_residence	Country where employee resides	String
remote_ratio	0 (No remote), 50 (Hybrid), 100 (Fully remote)	Integer
required_skills	Top 5 required skills (comma-separated)	String
education_required	Minimum education requirement	String
years_experience	Required years of experience	Integer
industry	Industry sector of the company	String
posting_date	Date when job was posted	Date
application_deadline	Application deadline	Date
job_description_length	Character count of job description	Integer
benefits_score	Numerical score of benefits package (1-10)	Float

Potential Use Cases

1. Salary Prediction Models

   • Build ML models to predict AI job salaries
   • Analyze factors affecting compensation
   • Compare salaries across different locations

2. Market Trend Analysis

   • Track the evolution of AI job market
   • Identify emerging job roles and skills
   • Analyze remote work adoption patterns

3. Career Planning

   • Understand skill requirements for different positions
   • Compare opportunities across countries
   • Plan career progression paths

4. Business Intelligence

   • Company hiring patterns analysis
   • Skills gap identification
   • Market competition insights

5. Geographic Studies

   • Cost of living vs. salary analysis
   • Regional market maturity assessment
   • Immigration pattern correlations

Data Collection Methodology

This is a synthetic dataset created for educational purposes to simulate AI job market patterns. All data is algorithmically generated based on industry research and market trends.

Sample Data Preview

job_id,job_title,salary_usd,experience_level,company_location,remote_ratio
AI001,Senior ML Engineer,145000,SE,United States,50
AI002,Data Scientist,89000,MI,Germany,100
AI003,AI Research Scientist,175000,EX,United Kingdom,0

Kaggle Dataset Tags

#artificial-intelligence #machine-learning #jobs #salary #career #data-science #employment #tech-industry #remote-work #compensation

Dataset Files Structure

ai-job-market-2025/
├── main_dataset.csv (15,247 rows)
├── skills_analysis.csv (skill frequency data)
├── company_profiles.csv (company information)
├── geographic_data.csv (country/city details)
├── time_series.csv (monthly trends)
└── data_dictionary.pdf (detailed documentation)

Acknowledgments

All personal information has been anonymized. This dataset is intended for educational and research purposes.

Discussion Topics for Community Engagement

1. What factors most influence AI job salaries in your analysis?
2. Interesting patterns you've discovered in remote work trends
3. Best visualization techniques for this type of employment data
4. Prediction model results - share your accuracy scores!
5. Geographic insights that surprised you

Suggested Notebook Titles for Analysis

• Predicting AI Salaries: A Complete ML Pipeline
• The Great Remote Work Shift in AI Jobs
• Skills That Pay: What Makes AI Engineers Valuable
• Global AI Talent Migration Patterns
• Company Size vs. Compensation: The AI Edition

*This dat...

Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

Number of employees by North American Industry Classification System (NAICS) and data type (seasonally adjusted, trend-cycle and unadjusted), last 5 months. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About This Dataset

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

About Local Boards

Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

Notes and Definitions

NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ Extensive datasets with job postings data from 5 leading B2B data sources;

➡️ Jobs API designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing the API.

✅ For HR tech

Job posting data can provide insights into the demand for different types of jobs and skills, as well as trends in job postings over time. With access to historical data, companies can develop predictive models.

✅ For Investors

Explore expansion trends, analyze hiring practices, and predict company or industry growth rates, enabling the extraction of actionable strategic and operational insights. At a larger scale of analysis, Job Postings Data can be leveraged to forecast market trends and predict the growth of specific industries.

✅ For Lead generation

Coresignal’s Job Postings Data is ideal for lead generation and determining purchasing intent. In B2B sales, job postings can help identify the best time to approach a prospective client.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Industry town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Industry town, the median income for all workers aged 15 years and older, regardless of work hours, was $49,429 for males and $24,321 for females.

These income figures highlight a substantial gender-based income gap in Industry town. Women, regardless of work hours, earn 49 cents for each dollar earned by men. This significant gender pay gap, approximately 51%, underscores concerning gender-based income inequality in the town of Industry town.

- Full-time workers, aged 15 years and older: In Industry town, among full-time, year-round workers aged 15 years and older, males earned a median income of $56,447, while females earned $33,508, leading to a 41% gender pay gap among full-time workers. This illustrates that women earn 59 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Industry town, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/industry-me-income-by-gender.jpeg" alt="Industry, Maine gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry town median household income by gender. You can refer the same here

Context

The top three occupations in the Restaurants & Food Services Industry Group are Waiters & waitresses, Cooks, Food service managers, Cashiers, and Food preparation workers. On average, full-time employees in the Restaurants & Food Services Industry Group work 42.9 hours per week and have an average annual salary of $32,261. Part-time employees in the same industry work 22 hours and earn an average annual salary of $10,493.

The locations with the highest concentration of employees in the Restaurants & Food Services Industry Group are Tallahassee City (Central) PUMA, FL, San Diego City (Central/Mid-City) PUMA, CA, and Kalamazoo & Portage Cities Area PUMA, MI. The industry that purchases the most products or services from the Hospitals Industry Group is Restaurants & Food Services.

Content

Here, The dataset that I have Produced has all the things mentioned about people working in the Food-Service Industry. It has data about Occupations, Wages, Opportunities, Diversity, and Input/Output in the Food-Service Industry. All the column description are given in the below dataset description.

Acknowledgements

Due to Covid-19 pandemic, people who were having jobs in restaurants, and had a single source of income had a very bad year, and no income during lockdown, which led to to give insights about this.

Use our Glassdoor dataset to find market trends and business information on companies as well as how current and past employees perceive and rate them. You may purchase the entire dataset or a customized subset depending on your needs. Popular use cases: competitive business intelligence, location-based marketing, geotargeting, B2B data enrichment, and more. The Glassdoor companies information dataset, one of the largest jobs and recruiting sites, offers a complete company overview with reviews and FAQs that provide insights about jobs and companies. The dataset includes all major data points: Location, Founding date, Revenue range, Size,Management, Company rating, CE outlook, Reviews, and FAQ as added by employees, Rating CEO approvalm and more.

Challenger Job Cuts in the United States decreased to 54064 Persons in September from 85979 Persons in August of 2025. This dataset provides the latest reported value for - United States Challenger Job Cuts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

As part of its mandate under Title VII of the Civil Rights Act of 1964, as amended, the Equal Employment Opportunity Commission requires periodic reports from public and private employers, and unions and labor organizations which indicate the composition of their work forces by sex and by race/ethnic category. Key among these reports is the EEO-1, which is collected annually from Private employers with 100 or more employees or federal contractors with 50 more employees. In 2007, over 67,800 employers with more than 61.3 million employees filed EEO-1 reports. The confidentiality provision which governs release of these data (Section 709 (e) of Title VII of the Civil Rights Act of 1964, as amended by the Equal Employment Opportunity Act of 1972) prohibits release of individually identifiable information. However, data in aggregated format for major geographic areas and by industry group for private employers (EEO-1) are available. The following tables are national aggregations by those industries with the greatest employment.

The Online Recruitment Sites industry has boomed since the 2000s as job searches have moved online and the internet has become an indispensable part of daily life. The internet has become the primary medium for communicating and accessing information, the main driving force behind this industry's rise. Job seekers and employers have increasingly turned to online recruitment sites to look for new openings and find new talent pools.The largest online recruitment sites have grown through organic innovation and by acquiring competitors targeting niche industries. Historically, incumbents held a competitive advantage in developing brand names, making it difficult for new sites to gain market share. Nonetheless, low barriers to entry have upended the industry as once-dominant platforms like Monster and CareerBuilder have lost relevance, and LinkedIn has become the overwhelming market-leader by leveraging technological innovation. Online job portals have become the primary tool for matching candidates to employers, with the pandemic only furthering the online shift as businesses embrace digital talent sourcing. In this environment, industry revenue is forecast to grow at a CAGR of 6.2% to $18.8 billion through 2025, including 6.4% in 2025 alone. Profitability has widened too, despite heavy ongoing investments in technology, with platforms relying on premium services to bring in recurring revenue streams.Driven by the rapid development of artificial intelligence and machine learning to automate resume screening, candidate sourcing and chat-based engagement, online recruitment sites will provide a broader range of services that go well beyond standard job posting services and resume collection. Predictive analytics will be central to the transformation of talent acquisition by replacing manual screening, helping recruiters compete more effectively with in-house hiring departments. Online recruitment sites will continue to evolve into professional networking platforms, becoming comprehensive career ecosystems. With a steady labor market poised to see growth in key sectors like healthcare and technology, revenue across online recruitment sites is forecast to grow at a CAGR of 5.6% to $24.8 billion through 2030.

As part of its mandate under Title VII of the Civil Rights Act of 1964, as amended, the Equal Employment Opportunity Commission requires periodic reports from public and private employers, and unions and labor organizations which indicate the composition of their work forces by sex and by race/ethnic category. Key among these reports is the EEO-1, which is collected annually from Private employers with 100 or more employees or federal contractors with 50 more employees. In 2007, over 67,800 employers with more than 61.3 million employees filed EEO-1 reports. The confidentiality provision which governs release of these data (Section 709 (e) of Title VII of the Civil Rights Act of 1964, as amended by the Equal Employment Opportunity Act of 1972) prohibits release of individually identifiable information. However, data in aggregated format for major geographic areas and by industry group for private employers (EEO-1) are available. The following tables are national aggregations by those industries with the greatest employment.

Annual employee and employment estimates for Great Britain and UK split by two, three and five-digit Standard Industrial Classification: SIC 2007. Results given by full-time or part-time and public or private splits.

This dataset has info on over 10,000 different companies from Ambition Box, a website that lets people share their experiences working at different companies.

The dataset includes:

Company name: The name of the company. Ratings: The overall star rating given by users. Total reviews: How many reviews the company has gotten. Average salary: The typical pay for workers at the company. Interviews taken: How many job interviews the company has done. Total jobs available: How many job openings the company has. Total benefits: Info on things like health insurance, vacation time, etc. that the company offers. Number of employees: How many people work at the company. Years in business: How long the company has been around. Industry type: What kind of business the company is in.

This is the monthly data for U.S. employment and unemployment by state including some numbers for Puerto Rico. This dataset was accessed on April 7th 2008. The data for February 2008 are preliminary. The data presented are seasonally adjusted although the unadjusted numbers are also available. Unavailable data are represented as -1. The dataset is taken from Tables 3 and 5 from the United States Department of Labor, Bureau of Labor Statistics. It includes the civilian labor force, the unemployed in numbers and percentages, and employment by industry. Data from table 3 "refer to place of residence. Data for Puerto Rico are derived from a monthly household survey similar to the Current Population Survey. Area definitions are based on Office of Management and Budget Bulletin No. 08-01, dated November 20, 2007, and are available at http://www.bls.gov/lau/lausmsa.htm. Estimates for the latest month are subject to revision the following month". Data from table 5 "are counts of jobs by place of work. Estimates are currently projected from 2007 benchmark levels. Estimates subsequent to the current benchmarks are provisional and will be revised when new information becomes available. Data reflect the conversion to the 2007 version of the North American Industry Classification System (NAICS) as the basis for the assignment and tabulation of economic data by industry, replacing NAICS 2002. For more details, see http://www.bls.gov/sae/saenaics07.htm.