Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

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.

The LinkedIn Jobs Listing dataset emerges as a comprehensive resource for individuals navigating the contemporary job market. With a focus on critical employment details, the dataset encapsulates key facets of job listings, including titles, company names, locations, and employment specifics such as seniority levels and functions. This wealth of information is instrumental for job seekers looking to align their skills and aspirations with the right opportunities. The inclusion of direct application links and real-time application numbers enhances the dataset's utility, offering users a streamlined approach to engaging with potential employers. Beyond aiding job seekers, the dataset serves as a valuable tool for analysts and researchers, providing nuanced insights into industry trends and the evolving demands of the job market. The temporal aspect, captured through job posting timestamps, allows for the observation of job trends over time. Moreover, the dataset's integration of company details, including unique identifiers and LinkedIn profile links, enables a deeper exploration of hiring organizations. Whether for job seekers or analysts, the LinkedIn Jobs Listing dataset emerges as a versatile and informative repository, empowering users with the knowledge to make informed decisions in their professional pursuits.

Job Offers in the United States increased to 7391 Thousand in April from 7200 Thousand in March of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Spain Job Offers Scraped Data

Uncovering Qualifications and Requirements

By [source]

About this dataset

This dataset contains valuable web scraping information about job offers located in Spain, and gives details such as the offer name, company, location, and time of offer to potential employers. Having this knowledge is incredibly beneficial for any job seeker looking to target potential employers in Spain, understand the qualifications and requirements needed to be considered for a role and know approximately how long an offer is likely to stay on Linkedin. This dataset can also be extremely useful for recruiters who need a detailed overview of all job offers currently active in the Spanish market in order to filter out relevant vacancies. Lastly, professionals who have an eye on the Spanish job market can especially benefit from this dataset as it provides useful insights that can help optimise their search even more. This dataset consequently makes it easy for users interested in uncovering opportunities within Spain’s labour landscape with access detailed information about current job opportunities at their fingertips

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This guide will help those looking to use this dataset to discover the job market in Spain. The data provided in the dataset can be a great starting point for people who want to optimize their job search and uncover potential opportunities available.

• Understand What Is Being Measured:The dataset contains details such as a job offer name, company, and location along with other factors such as time of offer and type of schedule asked. It is important to understand what each column represents before using the data set.
• Number of Job Offers Available:This dataset provides an insight on how many job offers are available throughout Spain by showing which areas have a high number of jobs listed and what types of jobs are needed in certain areas or businesses. This information could be used for expanding your career or for searching for specific jobs within different regions in Spain that match your skillset or desired salary range .
• Required Qualifications & Skill Set:The type of schedule being asked by businesses is also mentioned, allowing users to understand if certain employers require multiple shifts, weekend work or hours outside the normal 9 - 5 depending on positions needed within companies located throughout the country . Additionally, understanding what skills sets are required not only quality you prioritize when learning new technologies or gaining qualifications but can give you an idea about what other soft skills may be required by businesses like team work , communication etc..
• Location Opportunities:This web scraping list allows users to gain access into potential companies located throughout Spain such as Madrid , Barcelona , Valencia etc.. By understanding where business demand exists across different regions one could look at taking up new roles with higher remuneration , specialize more closely in recruitments/searches tailored specifically towards various regions around Spain .

By following this guide, you should now have a robust understanding about how best utilize this dataset obtained from UOC along with an increased knowledge on identifying job opportunities available through webscraping for those seeking work experience/positions across multiple regions within the country

Research Ideas

• Analyzing the job market in Spain - Companies offering jobs can be compared and contrasted using this dataset, such as locations of where they are looking to hire, types of schedules they offer, length of job postings, etc. This information can let users to target potential employers instead of wasting time randomly applying for jobs online.
• Optimizing a Job Search- Web scraping allows users to quickly gather job postings from all sources on a daily basis and view relevant qualifications and requirements needed for each post in order to better optimize their job search process.
• Leveraging data insights – Insights collected by analyzing this web scraping dataset can be used for strategic advantage when creating LinkedIn or recruitment campaigns targeting Spanish markets based on the available applicants’ preferences – such as hours per week or area/position within particular companies typically offered in the datas set available from UOC

Acknowledgements

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

L...

Employment Rate in the United States decreased to 59.70 percent in May from 60 percent in April of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Occupational Skills and Tasks

Understanding the Role of Skills in Online Job Ads

By [source]

About this dataset

This dataset provides an invaluable resource to better understand the connection between occupational skills and related tasks associated with them. Drawing from online job advertisements, it reflects how the range of skills and tasks an individual needs to have within a job role changes over time. The data has been reconciled with the JRC-Eurofound Task Taxonomy, making this dataset a powerful tool for researchers who are looking to understand an occupation's profile and competency requirements. This includes two columns SKILL and TASK which provide descriptors that have been reconciled with the Task Taxonomy respective to their positions respectively. With such insights found in this data, one can not only recognize skilled-based jobs along bettering their hiring practices but also facilitate a more holistic understanding of talent identification during modern recruitment processes

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Get familiar with the two columns - SKILL and TASK. The SKILL column describes skill descriptors found in online job advertisements that have been reconciled with the JRC-Eurofound Task Taxonomy, whilst TASK provides the task for each skill description entry.
• Explore how different occupations rely on different sets of skills/tasks or look into trends over time by examining datasets from different years or by filtering them by type/labour market.
• Consider utilizing data visualization techniques like heat maps in order to more easily recognize patterns in large data sets such as those found in this dataset
• Make sure you check out other similar datasets available on kaggle's platform (e.g., Education, Professional Background), as they may have useful connections or overlap with this one based on common data points like geography/location, occupation type etc..

By following these tips you’ll be able to benefit more fully from this great resource!

Research Ideas

• Analyzing the correlation between specific jobs and growth rate of certain skills over time.
• Examining how certain skills may be trending in a particular job market or industry sector.
• Comparing and contrasting occupational skill profiles between different professions or geographical locations to better allocate resources appropriately for hiring and training purposes

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: skill_task_dictionary.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | SKILL | A description of the skill required for the job. (Text) | | TASK | A description of the task associated with the skill. (Text) |

Acknowledgements

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

The jobactive Job Placements table provides data on jobactive job placements. The table includes provider information, vacancy details, job seeker characteristics at the time of the job placement and job placement to outcome conversion denominators and numerators. The lowest data grain in the dataset is JOB_PLACEMENT_ID, a unique code generated each time a job seeker is referred to a vacancy. The dataset was extracted on 5 August 2018, however is based on job placements confirmed between 1 July 2016 and 30 June 2017.
Please note that the time period of the dataset has been restricted to mitigate any potential sensitivity risks and this may limit certain analyses.

Full Time Employment in the United States decreased to 134840 Thousand in May from 135463 Thousand in April of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Content

This dataset contains job listings across many data science positions which includes data scientist, machine learning engineer, data engineer, business analyst, data science manager, database administrator, business intelligence developer and director of data science in the US. There are 1200 rows and 9 columns. The column headings are job title, company, location, rating, date, salary, description (summary), links and descriptions (full). The data was web scraped from indeed web portal on Nov 20, 2022 using the indeed API.

Potential tasks

Datasets like this could help sharpen your skills in data cleaning, EDA, feature engineering, classification, clustering, text processing, NLP etc. There are many NaN entries in the salary column as most job listings do not provide salary info, can you come up with a way to fill those entries? The last column (descriptions) contains the full job description, with this at your disposal, there is an infinite number of features you could extract such as skill requirement, education, experience, etc. Can these features be utilized in a skill clustering analysis to guide curriculum development? Can you deploy a classification model for salary prediction? What other insight can you glean from the data? Have fun playing with the dataset. Happy learning!

Original Data Source: Data Science Job Postings (Indeed USA)

This dataset provides a comprehensive view of the job market, highlighting the companies and cities that have the highest number of job opportunities.

The JoPilot dataset is a valuable resource for anyone interested in the job market and provides a comprehensive view of the employment landscape across different industries and regions.

This dataset was created by JoPilot and contains information on the number of jobs by company and city in California, with features such as:

• Company name • City • State • Number of active jobs

Job Vacancies in Japan decreased to 812.45 Thousand in February from 846.79 Thousand in January of 2025. This dataset provides - Japan Job Vacancies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Job Vacancies in France decreased to 249.30 Thousand in May from 283.80 Thousand in April of 2025. This dataset provides - France Job Vacancies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

🧠 data_jobs Dataset

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

  Background

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

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

Techsalerator’s Job Openings Data in Africa offers a comprehensive and insightful dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a thorough view of employment opportunities across the African continent. This dataset aggregates job postings from a wide range of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Africa.

Key Features of the Dataset: Broad Coverage:

The dataset aggregates job postings from numerous sources including company career pages, job boards, recruitment agencies, and professional networking sites. This extensive coverage ensures a broad spectrum of job opportunities from multiple channels. Daily Updates:

Job posting data is updated daily, providing real-time insights into the job market. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This categorization allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

The dataset includes detailed information on job openings across different countries and regions within Africa. This regional breakdown helps users understand job market dynamics and opportunities in various geographic locations. Role and Skill Insights:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in finding opportunities that match their expertise and helps recruiters identify candidates with the desired skill sets. Company Information:

Users can access details about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where the demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. African Countries Covered: Northern Africa: Algeria Egypt Libya Mauritania Morocco Sudan Tunisia Sub-Saharan Africa: West Africa: Benin Burkina Faso Cape Verde Ivory Coast (Côte d'Ivoire) Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Central Africa: Angola Cameroon Central African Republic Chad Congo, Republic of the Congo, Democratic Republic of the Equatorial Guinea Gabon São Tomé and Príncipe East Africa: Burundi Comoros Djibouti Eritrea Eswatini (Swaziland) Ethiopia Kenya Lesotho Malawi Mauritius Rwanda Seychelles Somalia Tanzania Uganda Southern Africa: Botswana Lesotho Namibia South Africa Swaziland (Eswatini) Zimbabwe Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the dataset to identify hiring trends, understand competitive practices, and refine recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, making their job search more efficient and targeted. Strategic Workforce Planning: Companies can gain valuable insights into the availability of talent across Africa, assisting with decisions related to market expansion, office locations, and talent acquisition. Techsalerator’s Job Openings Data in Africa is a critical resource for understanding the diverse and evolving job markets across the continent. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.

The Job Skills Correlation dataset is a collection of data that provides insights into the relationships between different job skills and how they relate to each other. The dataset offers a valuable resource for researchers, policymakers, and employers interested in understanding the interdependencies between different job skills and their impact on job performance and success.

The dataset contains information about the correlation between different job skills, including technical skills, soft skills, and industry-specific skills. The dataset includes data for a wide range of occupations, from healthcare and technology to manufacturing and retail.

The dataset is particularly useful for researchers interested in understanding the skills required for different jobs and how these skills interact with each other. Policymakers can also use the dataset to develop strategies to promote skill development and training programs that take into account the interdependencies between different job skills.

Employers can also benefit from the dataset by identifying the skills that are most closely related to job success and performance in their industry. By understanding the correlations between different job skills, employers can develop more effective job training and recruitment programs that target the most relevant skills.

Overall, the Job Skills Correlation dataset is an essential resource for anyone interested in understanding the complex relationships between different job skills and their impact on job performance and success. By providing insights into the correlations between different job skills, the dataset can help individuals and organizations make more informed decisions about training, hiring, and career development.

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Context

This dataset was created by PromptCloud and DataStock. This dataset holds around 30K records for the date range of 1st May 2019 to 31st July 2019.

You can download the full dataset here.

Content

This dataset contains the following:

• Total Records Count : 1203379 
• Domain Name: indeed.com 
• Date Range: 01st May 2019 - 31st Jul 2019  
• File Extension: LDJSON
• Available Fields : -- uniq_id, -- crawl_timestamp, -- url, -- job_title, -- category, -- company_name, -- city, -- state, -- country, -- post_date, -- job_description, -- job_board, -- geo, -- inferred_city, -- inferred_state, -- inferred_country, -- fitness_score 

Acknowledgements

We wouldn't be here without the help of our in house web scraping team at PromptCloud and DataStock. Please feel free to reach out to us at marketing@promptcloud.com