The AI/ML industry is rapidly evolving, and companies worldwide are actively hiring Data Scientists, ML Engineers, AI Researchers, and Quant Analysts. This dataset provides 2,000+ synthetic but realistic job listings that capture important details like:

• Company information

• Industry domain

• Job titles & experience levels

• Required skills & tools

• Salary ranges (USD)

• Location & employment type

• Posting dates (2023–2025)

This dataset is designed to help researchers, students, and practitioners analyze trends in the AI job market and build real-world projects such as salary prediction, skill-demand analysis, and workforce analytics.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by data type (seasonally adjusted and trend-cycle), gender and age group. 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This monthly compendium of statistics and articles on the Labour Market was been replaced by the Economic and Labour Market Review.

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: LMT

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

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

Labour market information including employment rates, unemployment rates and zero hour contracts for those aged 16 and above in the UK, split by age group.

Data and analysis of Washington's employment conditions, economy, job market and workforce. These reports can help make informed career, hiring and policy decisions.

The Employment Growth LinkedIn dataset contains information about job postings related to employment growth, including:

About Column: - isic_section: A to X - isic_section_name: Agriculture; forestry and fishing, Mining and quarrying - isic_division: Show the division number. - isic_division_name: Crop and animal production, hunting and related service activities, Mining of coal and lignite, - industry_sk - industry_name: ranching, mining & metals, oil & energy, dairy, farming, etc - Industry_group_sk: Show the digits. - Industry_group_name: Manufacturing, Corporate services, Consumer goods,

This dataset can be used to analyze:

• Job market trends and growth areas
• In-demand skills for employment growth
• Geographic distribution of employment growth
• Industry-specific employment growth metrics

*This dataset can be used for various purposes such as: * - Job market analysis - Workforce planning - Economic development - Research and development

A summary of metrics to understand changes within occupations between 2016 and 2021, with a look at movements in and out of the workforce in addition to workforce demographics.

Discover the "Job.com USA Jobs Dataset," a detailed resource that provides an in-depth look at the job market in the United States.

This dataset is sourced from Job.com, a leading employment platform in the USA, and includes comprehensive information on job listings across various industries and regions.

Key Features:

• Extensive Job Listings: Features a wide range of job postings from different sectors and industries, offering a comprehensive overview of employment opportunities across the United States.
• Detailed Information: Each listing includes important details such as job titles, company names, job descriptions, locations, employment types (full-time, part-time, remote, contract), required qualifications, and salary data.
• Insights into Market Trends: Analyze current trends in the US job market, including in-demand skills, leading employers, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research and Analysis: This dataset is perfect for researchers, HR professionals, and data analysts interested in studying labor market trends, developing recruitment strategies, or understanding the employment dynamics in the USA.

The Job.com USA Jobs Dataset offers valuable insights into the American job market, making it a crucial resource for job seekers, employers, and researchers alike. Use this dataset to stay ahead of market trends, explore employment opportunities, and gain a deeper understanding of job market dynamics in the United States.

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.

Labor Market Conditions Index in the United States decreased to 1.50 Index Points in June from 3.30 Index Points in May of 2017. This dataset provides the latest reported value for - United States Labor Market Conditions Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

An essential resource for all users of UK economic and labour market statistics. It draws together the expert research and analysis and range of content found in Economic Trends and Labour Market Trends to build an up-to-date, comprehensive and unique statistical picture of the UK economy and labour market. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: ELMR

Job Trends in Australia

Insight into Job Types, Salaries and Growth Across States

By [source]

About this dataset

This dataset provides an insightful look into the Australian job market and the country's current trend of economic development. It consists of 30000 unique job postings from SEEK Australia, a renowned job board in Australia, offering valuable insights regarding salaries, job types, and openings across cities and states.

The data allows researchers to compare which type of jobs are offered across different locations, providing critical information on which cities or states offer particular kinds of jobs. Moreover, it offers a framework that can be used to understand how different companies compare when it comes to salaries and hiring practices. In addition, this dataset provides an in-depth view into what type of job openings there are in each city or state and their respective salaries. All this is available through reliable columns such as city, state, company name, salary offered & url enabling effective analysis and providing consumers with much needed knowledge about their potential employment opportunities in the market

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For more datasets, click here.

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How to use the dataset

This dataset can be used to gain insights into the Australian job market – from job types, salaries, and locations to trends in growth across states. To get started working with this data, you will need to download the dataset from Kaggle.

Once you have the dataset downloaded, it is important to become familiar with the different fields available as these will influence what kind of analysis you can do. The columns include category, city, company name, geo coordinates (for location-based analysis), job board (to determine where these jobs were posted), job description (to find relevant keywords and topics related with each position), job title (to search for specific jobs or trends in titles over time), job type (i.e full time/part-time etc.), posting date and salary offered.

You can further filter your results based on any combination of these different column values to get more targeted information about a certain area or topic that you are researching on. Additionally, visualizing certain elements such as salary ranges by region/job type may be helpful for gaining a wide understanding of Australia’s labor landscape in various sectors and cities.

Finally, it may also be useful to look at how salaries might have changed over time by comparing postings from 2 different years for example which could help identify employment growth areas or opportunities for businesses looking to set up shop in certain regions etc

Research Ideas

• Analyzing Salary Trends: By investigating the salaries of various job postings, researchers can gain insights on wage growth and wage disparities across different cities and states in Australia.
• Comparing Job Types & Salaries: Researchers can observe which cities offer higher salaries for particular job types and also get an understanding of what is expected from potential applicants.
• Tracking Job Market Growth: By using data from the years before, it is possible to identify which areas have seen the most growth in terms of job opportunities and how that compares with other areas in Australia

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: seek_australia.csv | Column name | Description | |:--------------------|:------------------------------------------------------------| | category | The category of the job posting. (String) | | city | The city in which the job is located. (String) | | company_name | The name of the company offering the job. (String) | | geo | The geographic coordinates of the job location. (String) | | job_board | The job board on which the job was posted. (String) | | job_description | The description of the job. (String) | | job_title | The title of the job. (...

This dataset provides a comprehensive overview of job market dynamics and unemployment trends across multiple regions from 2019 to 2023. It includes monthly data on unemployment rates, the number of active job postings, and the top in-demand skills within each region. Additionally, the dataset captures workforce demographics such as average age and education levels of unemployed workers, alongside indicators for retraining program needs.

The dataset is designed to support analysis and modeling tasks such as predicting unemployment hotspots, identifying skill gaps, and recommending targeted retraining or upskilling programs. It serves as a valuable resource for labor economists, policymakers, workforce development agencies, and data scientists aiming to understand and address regional employment challenges.

Australia Industrial Trends Survey: Composite Index: Labour Market data was reported at 43.900 Index in Mar 2025. This stayed constant from the previous number of 43.900 Index for Dec 2024. Australia Industrial Trends Survey: Composite Index: Labour Market data is updated quarterly, averaging 48.800 Index from Sep 2014 (Median) to Mar 2025, with 43 observations. The data reached an all-time high of 60.100 Index in Sep 2017 and a record low of 29.400 Index in Jun 2020. Australia Industrial Trends Survey: Composite Index: Labour Market data remains active status in CEIC and is reported by Australian Chamber of Commerce and Industry. The data is categorized under Global Database’s Australia – Table AU.S043: Australian Chamber-Westpac Industrial Trends Survey.

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.