Job Dataset

This dataset provides a comprehensive collection of synthetic job postings to facilitate research and analysis in the field of job market trends, natural language processing (NLP), and machine learning. Created for educational and research purposes, this dataset offers a diverse set of job listings across various industries and job types.

Descriptions for each of the columns in the dataset:

1. Job Id: A unique identifier for each job posting.
2. Experience: The required or preferred years of experience for the job.
3. Qualifications: The educational qualifications needed for the job.
4. Salary Range: The range of salaries or compensation offered for the position.
5. Location: The city or area where the job is located.
6. Country: The country where the job is located.
7. Latitude: The latitude coordinate of the job location.
8. Longitude: The longitude coordinate of the job location.
9. Work Type: The type of employment (e.g., full-time, part-time, contract).
10. Company Size: The approximate size or scale of the hiring company.
11. Job Posting Date: The date when the job posting was made public.
12. Preference: Special preferences or requirements for applicants (e.g., Only Male or Only Female, or Both)
13. Contact Person: The name of the contact person or recruiter for the job.
14. Contact: Contact information for job inquiries.
15. Job Title: The job title or position being advertised.
16. Role: The role or category of the job (e.g., software developer, marketing manager).
17. Job Portal: The platform or website where the job was posted.
18. Job Description: A detailed description of the job responsibilities and requirements.
19. Benefits: Information about benefits offered with the job (e.g., health insurance, retirement plans).
20. Skills: The skills or qualifications required for the job.
21. Responsibilities: Specific responsibilities and duties associated with the job.
22. Company Name: The name of the hiring company.
23. Company Profile: A brief overview of the company's background and mission.

Potential Use Cases:

• Building predictive models to forecast job market trends.
• Enhancing job recommendation systems for job seekers.
• Developing NLP models for resume parsing and job matching.
• Analyzing regional job market disparities and opportunities.
• Exploring salary prediction models for various job roles.

Acknowledgements:

We would like to express our gratitude to the Python Faker library for its invaluable contribution to the dataset generation process. Additionally, we appreciate the guidance provided by ChatGPT in fine-tuning the dataset, ensuring its quality, and adhering to ethical standards.

Note:

Please note that the examples provided are fictional and for illustrative purposes. You can tailor the descriptions and examples to match the specifics of your dataset. It is not suitable for real-world applications and should only be used within the scope of research and experimentation. You can also reach me via email at: rrana157@gmail.com

In this project, rich administrative data on search and recruitment from a low-wage online job portal are used to study the labor market impacts of COVID-19 in India. The data from the job portal includes information on vacancies and job seekers across 2019 and 2020. It covers all users that either posted a vacancy or applied to a job on the portal across the two years. The following datasets are available: Aggregate data State level data Each dataset reports the following details: Vacancies: Number of vacancies; number of full-time vacancies; average minimum salary for full-time vacancies; number of full-time vacancies above minimum salary offer of Rs. 15,000; average minimum experience for full-time vacancies Job seekers: Number of job seekers; Number of job seekers by gender, age and education

The contents of the dataset relate to trends in recruitment to work in the province of Trento. The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-and-society/Labour-market/Recruitment-at-work/Year-2017 The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html ATTRIBUTION: data compiled by the Labour Market and Policy Studies Office on data from the Labour Agency (Employment Centres) – Autonomous Province of Trento.

The 3-year Employment Outlooks consist of a rating (very good, good, moderate, limited or very limited) of the employment prospects as well as a narrative text that provides an assessment of the main forecast indicators, recent statistics, and value-added regional observations. Employment Outlooks are developed for each detailed occupation in all provinces, territories and economic regions of Canada, where data permits. They are updated annually. The Employment Outlooks developed until the 2015-2017 period were assessed on the basis of the National Occupational Classification (NOC) 2006, and include up to 520 occupations. Beginning with the 2016-2018 Outlooks, the NOC 2011 is used for the analysis and the Outlooks include up to 500 occupations. Outlooks and trend descriptions for the latest year (currently disseminated on Job Bank) are subject to change as new information becomes available. Every effort will be made to keep the records on the Open Data Portal as up to date as possible, though delays may occur. If you have comments or questions regarding the 3-year Employment Outlooks, please contact the Labour Market Information division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

Number of persons in the labour force (employment and unemployment) and unemployment rate, by North American Industry Classification System (NAICS), gender and age group.

The global job recruitment software market is booming, projected to reach $40 billion by 2033, driven by remote work, AI, and the need for efficient hiring. Learn about key trends, leading companies (Workday, Jobvite, Recruitee), and market challenges in this comprehensive analysis.

Discover the booming Job Search & Recruitment Services market! Our comprehensive analysis reveals a $50B market in 2025, projected to reach $90B by 2033, driven by digitalization and remote work. Explore key trends, regional insights, and leading companies shaping this dynamic industry.

The contents of the dataset are related to the trend in employment levels expected in the province of Trento. The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-and-society/Labour market/Recruitment forecasts/Year-2018 The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST. The indent ”-“ replaces the unpublished data as not available or not determinable or not publishable to protect the confidentiality of the statistical data (for values less than or equal to 5) or, in the case of sampling values, unreliable. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated. ATTRIBUTION: data processed by the Office for the Study of Labour Policies and Market on Unioncamere-Ministry of Labour data, Excelsior Information System.

The global Job Recruitment Platform market is poised for significant expansion, projected to reach an estimated market size of $5,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 12.5% anticipated to carry through the forecast period of 2025-2033. This impressive growth is primarily fueled by the increasing adoption of digital solutions by both employers and employees, a demand for streamlined hiring processes, and the growing need for efficient talent acquisition in a competitive labor market. The "For Employee" segment is experiencing a surge, driven by job seekers leveraging these platforms for wider reach and personalized job matching. Similarly, the "For Employer" segment is expanding rapidly as businesses recognize the cost and time efficiencies offered by these platforms in managing the entire recruitment lifecycle. Emerging economies, particularly in the Asia Pacific region, are emerging as key growth engines due to their expanding digital infrastructure and a burgeoning young workforce actively seeking employment opportunities. The shift towards remote and hybrid work models further amplifies the reliance on online recruitment tools. The market landscape is characterized by a dynamic interplay of innovation and established players. Subscription-based recruitment platforms are gaining traction due to their predictable cost structures and comprehensive feature sets, catering to businesses of all sizes. Free recruitment platforms, while offering accessibility, are increasingly integrating premium features to monetize their user base. Key market drivers include the growing need for automation in HR processes, the rise of AI-powered recruitment tools for candidate sourcing and screening, and the increasing globalization of the workforce, necessitating cross-border recruitment solutions. However, challenges such as data privacy concerns, the potential for information overload, and the need for continuous platform updates to keep pace with evolving hiring trends present strategic considerations for market participants. Nonetheless, the overall outlook remains overwhelmingly positive, with ample opportunities for growth and innovation within the job recruitment platform sector. This report provides an in-depth analysis of the global Job Recruitment Platform market, a rapidly evolving sector projected to witness significant expansion. Covering the period from 2019 to 2033, with a base year of 2025 and an extended forecast period of 2025-2033, this study leverages historical data from 2019-2024 to deliver actionable insights. We delve into market dynamics, identify key growth drivers and challenges, and project future trends, offering a valuable resource for stakeholders seeking to navigate this dynamic landscape. The estimated market size in 2025 is valued in the hundreds of millions of dollars, with robust growth anticipated throughout the forecast period, reaching billions by 2033.

Abstract

The School-to-Work Transition Survey (SWTS) was implemented by the Uganda Bureau of Statistics (UBOS) with funding from the Work4Youth partnership between the International Labour Organisation (ILO) Youth Employment Programme and the MasterCard Foundation. In Uganda the first round of the survey was conducted in 2013 and the second round took place between January and April 2015. This report presents the highlights of the second round of SWTS and compares the results to those of the first round. The analysis is updated and expanded to supplement the portrait of the youth labour market situation in Uganda presented in the first survey report. The report also outlines the institutional framework and relevant employment policies in the country. The SWTS is a unique survey instrument that generates relevant labour market information on young people aged 15 to 29 years, including longitudinal information on transitions within the labour market. The SWTS thus serves as a unique tool for demonstrating the increasingly tentative and indirect paths to decent and productive employment that today’s young men and women are facing. The SWTS serves a number of purposes:

• First, it detects the individual characteristics of young people that determine labour market disadvantage. This, in turn, is instrumental to the development of policy response to prevent the emergence of risk factors, as well as measures to remedy those factors that negatively affect the transition to decent work.
• Second, it identifies the features of youth labour demand, which help determine mismatches that can be addressed by policy interventions.
• Third, in countries where the labour market information system is not developed, it serves as an instrument to generate reliable data for policy-making and for monitoring progress towards the achievement of MDG1. In countries with a reasonably developed labour market information system, the survey helps to shed light on areas usually not captured by household-based surveys, such as youth conditions of work, wages and earnings, engagement in the informal economy, access to financial products and difficulties experienced by young people in running their business.

Geographic coverage

Whole country.

Analysis unit

• Individuals
• Households

Universe

A purposive sample refers to selection of units based on personal judgement rather than randomization.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the SWTS can be one of two types. The first type is a list of all members of the target population, while the second type is a method of selecting any member of this population. Sampling frames for the general population can be electoral rolls, street directories, telephone directories and customer lists from utilities which are used by almost all households, such as water, electricity, sewerage, and so on. It is preferable to use a list that is the most accurate, complete and up to date. The nature of this list is expected to differ from country to country. Some countries use a list of households, while other countries use a list of people.

Sampling deviation

• First stage: In the first stage, the whole country may be divided into administrative regions, such as governorates or provinces. Then a sample of these regions is selected, preferably using a purposive sampling technique to guarantee representativeness. A maximum variation technique, which is described earlier, can be used in the sample selection. Financial, accessibility and time constraints should be taken into consideration in the selection of the first-stage sample.

• Second stage: In this stage, each administrative region selected in the first stage may be divided into localities or census enumeration areas (EAs), and a sample of these areas is selected using a stratified technique. The units selected at this stage are usually called primary sampling units (PSUs). At this stage, a frame of PSUs is needed which a) lists the units covering the entire population in each selected administrative region exhaustively and without overlaps, and b) provides information for the selection of units efficiently, such as maps and good household listings. This frame is usually called the primary sampling frame (PSF). A self-weighted stratified systematic sampling technique is recommended in the selection of the PSUs. Self-weighted means that the number of PSUs selected from each administrative region should be proportionate to the population size in this region. In this stage, good maps and descriptions for identification and demarcation for each PSU are needed, together with up-to-date information on their size and characteristics.

• Third stage: The third stage may consist of dividing each of the PSUs selected in the second stage into smaller areas such as blocks, and then selecting one or more of these third-stage units (TSUs) from each selected PSU. This process may continue until a sample of sufficiently small ultimate area units (UAUs) is obtained. Again, self-weighted stratified systematic sampling techniques are recommended in the selection of the UAUs. The choice of the type of area units to be used in the survey, and the number of such units to be selected for the sample, are very important issues since the type of units chosen to serve as the PSUs and other higher-stage units can greatly affect survey quality, cost and operation. Here we present some general advice in the choice of such units. Firstly, it is not necessary to use units of the same type or size as PSUs in all governorates. Secondly, the survey team should not confuse the formal administrative label with the actual type of units involved.

• Fourth stage: At this stage, which is the last stage, in each selected sample area (or UAU) individual households may be listed and a sample selected with households as the ultimate sampling units (USUs). In the survey, information are collected and analysed for the USUs themselves including youth in the target age group, or just individual youth within sample households. A systematic sampling technique is recommended in the selection of the households in this stage if a list of all households in the UAU is available.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is designed to gather general information – personal, family and household information and education, activity history and aspirations from the respondent and then information relevant to the respondent’s current economic activity (whether still in school, unemployed, employed or outside of the labour force and not in school). The structure and flow of the questionnaires are as follows:

                                                       Structure and length of the questionnaire for youth sample

Section Number of questions in section Maximum number of questions asked of the individual

A Reference details (filled in by surveyors N.A. N.A. and used for control purposes)

B Personal, family and household information 20 20

C Education, activity history and aspirations 20 20 Based on response at end of section C, respondent jumps to section D, E, F or G

D Youth in education 7 47 E Unemployed youth 22 62 F Young employees, employers 48 (employees), 88 (employees), and own account workers 52 (self-employed) 92 (self-employed) G Youth not in the labour force 5 45

                                       Structure and length of the questionnaire for employer

Section Number of questions in section

A Reference details (filled in by surveyors and used for control purposes) N.A.

B Characteristics of the enterprise 15 C Recruitment and employment of young people 13 D Education and training of workers

Discover the booming online recruitment platform market! This in-depth analysis reveals a $46.18B (2025) market projected for significant growth, driven by AI, remote work, and ATS adoption. Learn about key players, market trends, and future forecasts.

Employment and Social Development Canada (ESDC) uses the models of the Canadian Occupational Projection System (COPS) and the National Occupational Classification (NOC, 2021 version) to develop projections of future trends in the numbers of job openings and job seekers by occupation at the national level. The projections allow for identifying those occupations that may face labour shortage or labour surplus conditions over the medium term. The latest projections cover the 2024 to 2033 period. For more information, explore: Canadian Occupational Projections System – ESDC

Discover the booming Job Recruitment Software market! Our in-depth analysis reveals a $15B market in 2025, projected to reach $40B by 2033 (CAGR 12%). Explore key drivers, trends, restraints, and leading companies like Jobvite, Workday, and Recruitee. Learn how AI, ATS, and CRM are transforming talent acquisition.

The contents of the dataset are related to the trend in employment levels expected in the province of Trento. The data, which come from various sources, were compiled by the Labour Market and Policy Studies Office for the drafting of the Annual Employment Report in the province of Trento, available as open content at the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-and-society/Labour market/Recruitment forecasts/Year-2019 The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST. The indent ”-“ replaces the unpublished data as not available or not determinable or not publishable to protect the confidentiality of the statistical data (for values less than or equal to 5) or, in the case of sampling values, unreliable. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated. ATTRIBUTION: data processed by the Office for Studies of Policies and Labour Market on data on Unioncamere-ANPAL data, Excelsior Information System

Abstract

Defining the school-to-work transition is a matter worthy of careful consideration since it is the definition that determines the interpretation. Most studies define the transition as the length of time between the exit from education (either upon graduation or early exit without completion) to the first entry into stable employment. But exactly what is meant by “stable employment”? The definition of the term and the subsequent measurement of the transition vary from study to study and from country to country. Some studies take as the end point the first moment of employment in any job and others apply qualitative elements such as first stable job (measured by contract type).

The ILO SWTS was designed in a way that applies a stricter definition of “stable employment” than is typically used in the genre. By starting from the premise that a person has not “transited” until settled in a job that meets a very basic criteria of “decency”, namely a permanency that can provide the worker with a sense of security (e.g. a permanent contract), or a job that the worker feels personally satisfied with, the ILO is introducing a new quality element to the standard definition of school-to-work transition.

The main objectives of the CSWTS 2012 are to collect detailed information on the country's employment of persons aged 15-29 years old disaggregated by urban and rural areas. The survey provides information on the national youth employment that can then be used to develop, manage and evaluate youth employment policies and programmes.

The CSWTS serves a number of purposes. First, it detects the individual characteristics of young people that determine labour market disadvantage. This, in turn, is instrumental to the development of policy response to prevent the emergence of risk factors, as well as measures to remedy those factors that negatively affect the transition to decent work. Second, it identifies the features of youth labour demand, which help determine mismatches that can be addressed by policy interventions. Third, in countries where the labour market information system is not developed, it serves as an instrument to generate reliable data for policy-making and for monitoring progress towards the achievement of MDG1. In countries with a reasonably developed labour market information system, the survey helps to shed light on areas usually not captured by household-based surveys, such as youth conditions of work, wages and earnings, engagement in the informal economy, access to financial products and difficulties experienced by young people in running their business. Finally, it provides information to governments, the social partners and the donor community on the youth employment areas that require urgent attention. Other specific objectives are: - Obtain data on personal, family and household information including financial situation, health problems, highest educational level of parents, and occupation of parents. - Collect data on formal education/training, activities history and aspirations of youth/persons aged 15-29 years, including education and training, full history of economic activities, main goal in life, and working criteria.
- Collect data on young workers including personal work details of business or place of work, employment status, wage and salaried workers (employees), self-employed workers, contributing family workers, perception, time related underemployment and other inadequate employment situations, future prospects, training in current activity, and the job search.
- Collect data on unemployed youth including seeking work criteria, length of job search, availability criteria, and details of job search. - Collect data on youth in education. - Collect data on youth not in the labour force.

Geographic coverage

National coverage

Capital city (Phnom Penh)

Urban, Rural

Analysis unit

Households Individuals

Universe

Youth 15-29 years of age

Kind of data

Sample survey data [ssd]

Sampling procedure

Total sample of 160 Enumeration Areas (EAs), of which 123 would be rural and 37 urban. With 16 households were selected in each EA and this would have given an overall sample size of 2,560 households. These sample EAs were selected from the sample EAs of Cambodia Labour Force and Child Labour 2011-2012 as a sampling frame.

According to the sample selection, the SWTS in Cambodia 2012 was conducted in ten Capital/Provinces namely, Phnom Penh, Banteay Meanchey, Battambang, Kampong Cham, Kampot, Koh Kong, Prey Veng, Preah Sihanouk, Siem Reap, and Takeo, with a representative sample of 2,560 households within 160 EAs. The survey is to collect information on various characteristics of youth aged 15 to 29 years.

The sample design for the survey was a stratified two-stage probability sample where the first stage units were enumeration areas (EAs) designated as the Primary Sampling Units (PSUs) and the second stage units as the Second Sampling Units (SSUs) were the households.

1. The first stage sampling selection

In this stage, enumeration areas (EAs) were selected with Systematic Random Sampling method. For the sample urban areas in each province, all numbers of urban areas were selected from the sampling frame. For the sample rural areas in each province, the method of Systematic Random Sampling with random start was used.

1. The second stage sampling selection

A fixed sample size of 16 households in each EA would be selected by using the method of Systematic Random Sampling with a random start.

For further details please refer to the technical document on sample selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. Questionnaire development Draft questionnaire for the Cambodia School-to-Work Transition Survey 2012 (SWTS) was developed based on guidelines of ILO Youth Employment Programme and Work for Youth Project model SWTS questionnaires.

2. Area of the pilot test The pilot test of the Cambodia School-to-Work Transition Survey 2012 was conducted in two provinces namely Kampong Speu and Takeo. Each province consists of 5 enumeration areas (EAs) and each EA was random selected 16 sample households having members aged 15-29 years. Totally, there were 120 youth households to be interviewed.

3. Recruitment Eight staffs were recruited for the pilot test. The pilot test was divided into 2 groups for the field operations in 2 provinces. Each group consisted of one supervisor and three enumerators for conducting in one province. All of these staff will be assigned as supervisors for the main survey.

4. Training of the pilot test Before going to the field of the pilot test, 8 staffs were received a three-day training on how to carry out data collection from 29 to 31 May 2012 at NIS. The training consisted of 2 days for training, 1 day for field-test of draft questionnaire, and reviewing of field-test. Observed difficulties and problems during field-test served as additional inputs for further revisions and improvement of the questionnaires and understanding.

5. Data collection of the pilot test The data collection of the pilot test was conducted from 12 to 16 June 2012. The EA map from the population census 2008, household listing form and the draft questionnaire were used in the pilot test.

First, selecting an EA where a leader of village lives and make updating listing of all households that are now living in a selected EA on the listing sheet given. Depending on the completed household listing sheet in the selected EA, a probability systematic random sampling of 8 households was used. 8 sample households were random selected from all households having members aged 15-29 years old.

1. Lessons learnt According to the fieldwork of pilot test, some points learned were stated as follows:
2. The engagement of the village leaders in the fieldwork made it possible to enjoin the active cooperation of households for the pilot test. They played a very important role in guiding and helping our fieldwork to the target.
3. Supervisors and enumerators should close cooperation with local authority or village leaders during the fieldwork. In general, before interviewing the village leaders have to inform first to the households or another word, the households can be interviewed while the local authority or village leader gave permission.
4. Providing of gift to village leaders and households during the field interview would encourage participating in the survey and welcome to answer the questions at any time. Moreover, the respondents will provide reliable information and gain close cooperation.
5. The time-period of interview was depended upon the types of the household members who aged 15-29 years and educational background or knowledge of the respondents.
6. Get long time for waiting the target persons who are employees.
7. Have to make interview at night time when the target persons work far away from home.
8. Having car for the field work that made easily transport and save time from and to villages as well as the households to be interviewed.
9. Difficulty of recalled answer seemed not reliable.

For details on the findings of the pilot test please refer to the attached report.

Cleaning operations

Upon submission of the completed questionnaires to NIS, those questionnaires were processed at the NIS. The training of data processing was carried out for 4 days from 9-12 August 2012. After training, the editing of the completed questionnaires was done manually starting from 13 August 2012. Data entry will be carried out after finishing data editing.

LinkedIn is a widely used professional networking platform that hosts millions of job postings. This dataset contains 1.3 million job listings scraped from LinkedIn in the year 2024.

This dataset can be used for various research tasks such as job market analysis, skills mapping, job recommendation systems, and more.

If you find this dataset valuable, please upvote 😊💼

This is the same master dataset that powers SkillExplorer

Interesting Task Ideas:

1. Practice data cleaning on raw data.
2. Analyze the most in-demand job titles or industries in different cities or countries.
3. Identify the top companies hiring for specific job positions.
4. Utilize the skills data to determine the most sought-after skills in different job categories.
5. Build a job recommendation system based on user profiles and job listing data.
6. Discover patterns in job types or levels across different industries.
7. Identify skill gaps in the job market to inform educational or training programs.
8. Explore the relationship between job title and required skills.

Photo by Clem Onojeghuo on Unsplash

Discover the booming job search software market! This in-depth analysis reveals a $5 billion (2025 est.) market projected to surpass $15 billion by 2033, driven by AI, cloud adoption, and remote work trends. Learn about key players, market segments, and future growth opportunities.