Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

Labour Market Profiles for Regions, Local Authorities, Local enterprsie Partnerships, Wards and Parliamentary Constituencies.

A Labour Market profile of an area.

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

🇬🇧 영국

Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

What Makes This Job Postings Data Unique

AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intelligence.

About Canaria Inc. ...

The job profiles are a description of tasks, responsibilities and functions that an individual has in a workplace that requires some knowledge, training, personal competencies, and in some cases professional experience.

This dataset includes data for an analysis of labor demand characteristics and workforce training needs in the metropolitan areas of La Paz-El Alto, Cochabamba, and Santa Cruz—large cities in Bolivia (Related publication only available in Spanish). This information is contrasted with a sample from intermediate and small cities in the country. Labor demand data for large cities comes from a survey of companies conducted in 2015 and 2016, while data for intermediate and small cities is derived from a survey conducted between 2016 and 2017. The document presents key findings on the productive characteristics of cities, company profiles, and workforce dynamics, including recruitment and selection processes, employee turnover, reasons for dismissals, training, demand for and valuation of skills, among other factors. Finally, it outlines policy implications for Bolivia’s labor market.

The Annual Social and Economic Supplement or March CPS supplement is the primary source of detailed information on income and work experience in the United States. Numerous publications based on this survey are issued each year by the Bureaus of Labor Statistics and Census. A public-use microdata file is available for private researchers, who also produce many academic and policy-related documents based on these data. The Annual Social and Economic Supplement is used to generate the annual Population Profile of the United States, reports on geographical mobility and educational attainment, and detailed analysis of money income and poverty status. The labor force and work experience data from this survey are used to profile the U.S. labor market and to make employment projections. To allow for the same type of in-depth analysis of hispanics, additional Hispanic sample units are added to the basic CPS sample in March each year. Additional weighting is also performed so that estimates can be made for households and families, in addition to persons.

The Current Population Survey (CPS) is the primary source of labor force statistics for the U.S. population. It is the source of numerous high-profile economic statistics, including the national unemployment rate, and provides data on a wide range of issues relating to employment and earnings. The CPS also collects extensive demographic data that complement and enhance our understanding of labor market conditions in the nation. The survey is jointly sponsored by the U.S. Census Bureau and the Bureau of Labor Statistics (BLS).

Employment by age and sex for UK regions and countries, rolling three-monthly figures published monthly, not seasonally adjusted. Labour Force Survey.

Data that captures industry and country-specific employment growth based on updates to LinkedIn member profiles.

This dataset is part of the LinkedIn - World Bank partnership, which helps governments and researchers understand rapidly evolving labor markets with detailed and dynamic data. It allows leaders to benchmark and compare labor markets across the world; analyze skills, occupations, migration, and industries; and leverage real-time data to make policy changes.

Visualizations for many of these data are available at linkedindata.worldbank.org. The data cover 2015-2019, are refreshed on an annual basis, and are available for 140 countries.

Additional experimental data is available by request via the Development Data Partnership.

In 2025, there were estimated to be approximately *** billion people employed worldwide, compared to **** billion people in 1991 - an increase of around *** billion people. There was a noticeable fall in global employment between 2019 and 2020, when the number of employed people fell from due to the sudden economic shock caused by the COVID-19 pandemic. Formal vs. Informal employment globally Worldwide, there is a large gap between the informally and formally employed. Most informally employed workers reside in the Global South, especially Africa and Southeast Asia. Moreover, men are slightly more likely to be informally employed than women. The majority of informal work, nearly ** percent, is within the agricultural sector, with domestic work and construction following behind. Women’s employment As the number of employees has risen globally, so has the number of employed women. Overall, care roles such as nursing and midwifery have the highest shares of female employees globally. Moreover, while the gender pay gap has shrunk over time, it still exists. As of 2024, the uncontrolled gender pay gap was ****, meaning women made, on average, ** cents per every dollar earned by men.

Employment Rate in the United States increased to 59.70 percent in September from 59.60 percent in August of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The data collection from jobseekers and firms is done as part of the enrollment and operations for the job search platform called “Job Talash”. As part of the operations for the platform, vacancies are listed from enrolled firms on the platform and match candidates who meet the requirements of the vacancy. Candidates are then invited to apply for the vacancies that they have been matched to. Firm Survey Dataset The Firm survey dataset consists of all the attempts made to employers to enlist vacancies on the platform. The dataset also has the ads listing data which specifies the requirements of firms for vacancies that are listed on the platform, Job Talash. All registered firms on the platform receive a call every 3 months, asking them if they’d like to list a vacancy on the platform. If they decide to list a vacancy, the information about the requirements for the vacancy is collected so that relevant candidates can be matched to those jobs. The Jobseeker Dataset The Jobseeker dataset is based on the job matches generated for the jobseekers periodically based on their profile that includes, work experience, gender, education level and job interest. These job matches are communicated to the jobseeker via text message and phone call. A screening instrument is used by the field team while making phone calls for giving job updates to the jobseekers and recording their interest in the available positions. Along with the application interest, also information is collected about whether they have been employed to earn an income in the last 14 or 30 days (randomized recall period for each jobseeker).

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

Sri Lankan Job Market Dataset (20,000 Records)

Predict Job Roles Based on Qualifications, Experience, and Skills

This synthetic Sri Lankan Job Market Dataset provides 20,000 records representing candidates with different degrees, fields of study, work experience, certifications, and skills. The dataset is designed to help explore how qualifications and experience influence the type of job a person is likely to get in Sri Lanka.

It is ideal for machine learning, classification, and data visualization projects, and can be used to predict job types based on personal and professional profiles.

Columns / Features

Why This Dataset is Special

• Large-scale synthetic data – 20,000 records for model training and testing
• Rich features – Combines education, skills, experience, and location
• Realistic correlations – Degree and field influence job type for predictive modeling
• Multi-purpose – Classification, exploratory data analysis, and visualization

Potential Applications

• Predict candidate job roles based on qualifications and skills
• Explore trends in education vs. employment
• Build ML models for career recommendation systems
• Data visualization to analyze skills, experience, and job distribution

This dataset provides information on labor demand and supply in the three metropolitan areas of Bolivia’s central axis. Labor demand data comes from two surveys conducted with companies in 2015 and 2016. Labor supply data comes from a census of university training centers (both professional and technical levels) and a survey of technical institutes.

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.