AI & ML Job Postings Dataset — LinkedIn & Indeed (2025)

This dataset contains time-stamped AI & ML job postings scraped from LinkedIn and Indeed over multiple days, covering companies, roles, and locations. It includes:

• link: URL to the job posting
• title: Job title (e.g., Data Scientist, ML Engineer)
• company: Company name
• location: City, state, or country
• date & time: Job posting timestamp
• scrape_date & scrape_time: When the data was collected

Dataset Highlights: - ~1,550 unique postings, clean and deduplicated - Ready for EDA, visualization, and ML experiments - Includes scrape metadata for temporal analysis

Potential Use Cases: - Trend analysis of AI/ML hiring over time - Skill extraction and NLP on job titles - Job classification or predictive modeling projects - Company hiring insights and labor market research - Geospatial analysis of AI/ML demand

Included Notebook: EDA_Job_Postings.ipynb
- Exploratory data analysis with top companies, job titles, locations, and word clouds - Time-series analysis of job postings

License: CC BY 4.0 — free for research, educational, and analysis purposes with attribution.

Note: Data was collected via public job postings; no personal candidate information is included. Users can further enrich the dataset using the job links if legally permissible.

Dataset

This dataset was created by RATNESH SATYARTHI

Released under Apache 2.0

Contents

This dataset contains a curated collection of job listings sourced from LinkedIn, featuring a variety of positions across multiple industries and locations. Each entry includes essential details such as job title, company name, job location, employment type, and base pay range, alongside a comprehensive job summary and required qualifications.

Key Features:

• Job Titles & Companies: Explore a wide range of roles from top companies including Amazon, AT&T, and PVH Corp.
• Location Data: Analyze job opportunities across various regions in the United States and the UK.
• Employment Types: Includes full-time, part-time, and contract positions, catering to diverse career preferences.
• Salary Information: Insights into base pay ranges for various roles, aiding in competitive salary analysis.
• Seniority Levels: From entry-level to mid-senior roles, this dataset covers multiple experience levels.

This dataset is ideal for researchers, data scientists, and job seekers looking to analyze job market trends, understand salary expectations, or develop predictive models for career growth. Use this resource to gain insights into the evolving job landscape and make informed career decisions.

This dataset contains 1,258 job postings collected from LinkedIn between 2019 and 2025. The dataset was compiled manually from jobs the author applied to and is used to study the transparency and structural characteristics of online job postings. The various attributes in the dataset are:

1. SENIORITY: - Entry-Level (Minimum 0 Years of Experience: such as 0-3 or 0-1 or 0-2), Junior (internships or 1-2 Years of Experience required), Associate (2-5 YoE),** Mid-Senior** (5-7 YoE), Senior (7+)
2. MIN_EXP: Minimum experience required: (2-5 YoE -> 2)
3. MAX_EXP: Maximum experience required: (1-5 YoE -> 5)
4. EXPERIENCE_R_P: Experience Preferred (P) or Required (R) or Both: P or R or Both
5. TITLE: Job Title given
6. COMMITMENT (Full-time/Intern/Part-time)
7. JOB_REQUIREMENT_COMPLEXITY: It depends on the person specification. If the person specification and skillset requirement spans several lengthy paragraphs, then the label is "Too Sophisticated". If it is basic and understandable, then the label is "Basic". Similarly, the labels are allocated on a scale of "Basic" to "Too Sophisticated".
8. SALARY_RANGE: if specified -> “Yes” otherwise -> "No"
9. CONTACT (any phone or email to contact for more information. Contact details specified for requesting special accommodations for the disabled don’t count)
10. TIMELINE: Recruitment timeline (from start to finish: information on rounds and dates)
11. LOCATION: Country where the job is based on
12. ACADEMIC_GRADE_REQ: Yes/No (Ex: > 9/10 GPA required or >70% required)
13. YEAR_POSTED: year in which the job was posted
14. REMARKS: Additional remarks about the job such as: Incomplete JD/Reposted/Two different work experience requirements specified in the same JD/Degree requirements unspecified/Skills unspecified
15. COMPANY_NAME: Name of the Company
16. BENEFITS: Did the company specify any benefits? If yes, then are they significant benefits (Ex: Pension, Healthcare, Bonus, Massive discounts etc). If yes then the label is "Yes" otherwise "No"
17. LinkedIn_Easy_Apply: Was the job through LinkedIn Easy Apply? If not then label is "NA". If yes then,
18. Application Viewed - "Viewed"
19. Resume downloaded - "Resume downloaded"
20. Contacted for the job - "Contacted"

If you use this dataset in your research, please cite: Zagabathuni, Y. (2025). LinkedIn Job Posting Transparency Dataset (2019–2025). Kaggle.

SIRTAR Job classification Dataset

This is a fusion of three known Kaggle datasets, added tons of preprocessing in the middle. These are the following:

LinkedIn Job Postings (2023-2024) [1]: https://www.kaggle.com/datasets/arshkon/linkedin-job-postings Indeed Job Postings: https://www.kaggle.com/datasets/spandanakalakonda/job-postings Jobstreet Job Postings: https://www.kaggle.com/datasets/azraimohamad/jobstreet-all-job-dataset

This dataset is used for the training of the new… See the full description on the dataset page: https://huggingface.co/datasets/daniel-jurado/job-title-classification-dataset.

🔍 Overview

This dataset contains detailed information on job postings sourced from LinkedIn, collected manually or via web scraping tools. It captures a variety of fields that offer insights into job market trends, in-demand skills, company hiring behavior, salary patterns, and geographical distributions.

This dataset is a controlled snapshot of the IBM HR Analytics Attrition dataset.

Source: https://www.kaggle.com/datasets/arshkon/linkedin-job-postings

Purpose: This copy is maintained to ensure reproducibility and stability of the analysis, avoiding dependency on external dataset changes.

Notes:

No transformations have been applied. This dataset represents a fixed version used in the project pipeline.

Zenodo description (paste into the Description field)

This is the Zenodo form description text, kept here for easy re-paste if the form is reset or a v2 release needs the same body. Not part of the release contents — lives at release/ rather than release/staging/.

Overview

A unified, analysis-ready panel of 114,954 US software-engineering and matched-control job postings drawn from three LinkedIn sources: two 2024 Kaggle snapshots (Arsh Koneru's LinkedIn Job Postings (2023–2024) and asaniczka's 1.3M LinkedIn Jobs & Skills (2024)) and a 2026 first-party scrape across 26 US metropolitan areas. The dataset accompanies a paper on AI-driven restructuring of software-engineering roles.

Every posting carries LLM-derived labels for seniority, years-of-experience floor, ghost-job assessment, an 8-enum skill-theme axis (people management, orchestration, verification, mentorship, performance, process scaffolding, legacy stack, context infrastructure), and a 17-enum role-family axis (frontend, backend, ML, AI/LLM engineer, devops, security, QA, and others). The frozen production prompts are included verbatim in the release.

Each posting with cleaned text also carries a 3072-dimensional text-embedding-3-large embedding computed over the title plus the boilerplate-removed description core.

What's included

• data/unified_core.parquet — the canonical analysis file, 114,954 rows × 35 columns.

• data/unified_core_observations.parquet — daily panel (one row per posting × scrape-date) for posting-duration work.

• scraped_raw/ — 363,060-posting near-raw fallback for the 2026 scrape, joinable by uid for researchers who want to redo cohort/preprocessing choices from scratch.

• prompts/ — the three frozen production LLM prompts (Stage 9 extraction, Stage 10 classification, Stage 12 skill-theme × role-family).

• scripts/rejoin_kaggle.py — restores raw 2024 descriptions byte-deterministically from upstream Kaggle source files.

• CODEBOOK.md, DATASHEET.md, ATTRIBUTIONS.md, croissant.json — full documentation, Gebru-style datasheet, license attributions, and MLCommons Croissant 1.0 metadata.

Cohort definition

Rows are the intersection of (a) the pipeline's deterministic balanced Stage-9 LLM frame and (b) a confirmed cohort label (LLM-confirmed SWE-or-adjacent or rule-based control). The canonical disjoint analysis frame is is_swe AND NOT is_control (59,954 SWE rows) versus is_control AND NOT is_swe (54,835 control rows); 165 overlap rows are kept in the file but typically excluded.

2024 description policy

The raw description column is null on rows where source ∈ {kaggle_arshkon, kaggle_asaniczka}. This avoids redistributing substantively copyrightable text from the upstream Kaggle datasets while preserving every derived column (cleaned text, embedding, all LLM labels) for all rows. The bundled rejoin script restores raw descriptions byte-identically from locally-downloaded upstream Kaggle files; see ATTRIBUTIONS.md for the derivative-work position and any downstream license obligations.

License and reuse

Released under CC BY 4.0 for data and documentation, MIT for the bundled scripts. Upstream attributions: Arsh Koneru (CC BY-SA 4.0) and asaniczka (ODC-By 1.0). The 2026 scrape is the depositor's first-party collection.

If you use this dataset, please cite this Zenodo record. Start with README.md, then CODEBOOK.md for column-level documentation.

Dataset

This dataset was created by Aniket@149Gupta

Released under MIT

Contents

Job Skill Set

Description

The Job Skill Set Dataset is designed for use in machine learning projects related to job matching, skill extraction, and natural language processing tasks. The dataset includes detailed information about job roles, descriptions, and associated skill sets, enabling developers and researchers to build and evaluate models for career recommendation systems, resume parsing, and skill inference.

Dataset Source

This dataset was initially sourced from the Kaggle dataset titled LinkedIn Job Postings by Arshkon. The original job postings data has been enhanced by extracting skill sets using RecAI API services. These APIs are designed for skill parsing, resume analysis, and other recruitment-related tasks.

Dataset

The dataset contains the following features: - job_id: A unique identifier for each job posting. - category: The category of the job, such as INFORMATION-TECHNOLOGY,BUSINESS-DEVELOPMENT,FINANCE,SALES or HR. - job_title: The title of the job position. - job_description: A detailed text description of the job, including responsibilities and qualifications. - job_skill_set: A list of relevant skills(include hard and soft skills) associated with the job, extracted using RecAI APIs.

Use Cases

This dataset is particularly useful for the following applications:

• Skill Extraction: Identifying and parsing skills from job descriptions.
• Job-Resume Matching: Matching job descriptions with potential candidate profiles.
• Recommendation Systems: Developing models that recommend jobs or training programs based on required skills.
• Natural Language Processing: Experimenting with text-based models in recruitment and career analytics.

License

Please consult the license information on the original Kaggle dataset page here.

Citation

If you use this dataset, please cite it as follows:

@misc{batuhan_mutlu_2024,
  title={job-skill-set},
  url={https://www.kaggle.com/dsv/10201355},
  DOI={10.34740/KAGGLE/DSV/10201355},
  publisher={Kaggle},
  author={Batuhan Mutlu},
  year={2024}
}

Dataset

This dataset was created by GhostJobs

Contents

Data science is a rapidly growing field in the tech industry, and LinkedIn is a popular platform for finding job opportunities in this domain.

This dataset provides valuable insights into data analyst job postings, including the required skills and software proficiency sought by employers.

If you find this dataset useful, don't forget to hit the upvote button! 😊💝

Checkout my top datasets

• Top Spotify Songs in 73 Countries
• Wages by Education in the USA
• Amazon Products Dataset (1.4M Products)
• TMDB 950K Movies
• Amazon Kindle Books (130K Books)

Interesting Task Ideas:

1. Analyze the most in-demand skills and software for data analyst positions.
2. Find common job titles in the data science field.
3. Explore the geographical distribution of data engineering job opportunities.
4. Identify the most sought-after skills

Photo by Lukas Blazek on Unsplash

Introduction: This dataset contains a collection of job postings scraped from Naukri.com, a popular job search and employment platform. The dataset provides valuable insights into the Indian job market, including job titles, company names, experience requirements, salary packages, job locations, and required skills. It serves as a valuable resource for job market analysis and job seekers seeking information about the employment landscape in India.

Features:

Job Titles: This column contains the job titles for various positions advertised on Naukri.com. Company Names: The names of the companies offering the job positions are provided in this column. Experience Required: The required experience level for each job listing is indicated in this column. Package: Information about the salary or compensation package for each job is detailed in this column. Locations: This column lists the job locations, providing insights into where these job opportunities are available. Skills: The column indicates the required skills or qualifications for each job posting. Job Link: The column indicates the actual Job link on Naukri.com. Post Time: The column indicates when this job was posted.

Source: This dataset was obtained by scraping job postings from Naukri.com. The data collection process was conducted on 24Oct, 2023 and 28Oct, 2023 using Python and Selenium. It is essential to note that the data's accuracy is based on the source website and may be subject to changes over time.

Usage: This dataset can be utilized for various purposes, including:

Analyzing trends in job titles and required skills in the Indian job market. Conducting salary and compensation package analyses. Gaining insights into regional job markets across India. Assisting job seekers in making informed decisions about potential job opportunities. Data Preparation: The dataset may have undergone some cleaning and preprocessing to ensure consistency and reliability.

Introduction The OpenWeb Ninja JSearch API offers a fast, reliable, and comprehensive real-time job postings data and salary data from Google for Job - the largest job aggregate on the web. The API sources job postings and salary data from LinkedIn, Indeed, Glassdoor, ZipRecruiter, Monster + all public job sites on the web.

The API supports several options and filters, including filtering by posting date, job title, location, remote jobs, job requirements, employer, and many other options. Each job posting includes 40+ job data points, including job title, job description, required experience, education, skills, job location, job expiration, and many other details.

See it in action here: https://google.com/search?gl=us&ibp=htl;jobs&q=marketing+in+texas.

This dataset is a subset of LinkedIn job offers from October 2023. It contains 2200 jobs from the DACH region, that appeared by either of the keyword searches Data analyst, data scientist, data engineer.

It contains the job title, company, job location, job description, and applicant number (bear in mind this is censored both below 25 and above 200 unfortunately).

There are additional variables that are non-essential and are simply binary variables, signaling weather a certain skill is mentioned in the job description or not (e.g. a 1 for SQL means it is mentioned as a skill, 0 means it is not).

Source: Job Description of LinkedIn Tag: Data Analyst Region: London, UK

I have scraped 115 job posts and collected the required skills for Data Analyst job posts on LinkedIn from September to December 2022.

Dataset of 422 jobs from LinkedIn to analyse data job market with search terms ("data analyst", "data scientist" & "data engineer")

Specifically interested in the application of NLP to extract in-demand tools in the market

Columns - 'job_title' - 'company_name' - 'post_date' - 'repost_date' - 'email', - 'number_of_employees' - 'job_desc' - 'num_applicants' - 'job_type' - 'job_level' - 'job_remote' - 'language' - 'salary' - 'sector' - 'link', - 'search_term'

Please note: This is only an initial dataset, further uploads with more rows with different search terms will be made in the future. For suggests or requests please make a comment.

Dataset

This dataset was created by Haider Ali Khan

Contents

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

Columns: job_id: A unique identifier for each job posting. Can be sourced from an external dataset (e.g., LinkedIn) or generated as a surrogate key during ETL to ensure each job row can be uniquely referenced. job_title_short: A simplified or standardized version of the job title (e.g., “Data Scientist”, “Data Analyst”) used for grouping or classification. job_title: The full, original job title as listed in the posting (e.g., “Senior Data Scientist – Machine Learning”). job_location: The location where the job is based, usually including city and/or state (e.g., “San Francisco, CA”). job_via: The platform, company, or recruitment source through which the job was posted (e.g., “via LinkedIn”, “via Indeed”). job_schedule_type: The type of job schedule, such as “Full-time”, “Part-time”, “Contract”, “Internship”, etc. job_work_from_home: Indicates whether the job allows remote work or work-from-home flexibility (Boolean or categorical: True / False / Hybrid). search_location: The geographic location or area used when searching or scraping for jobs (e.g., “New York”, “London”). Often used to contextualize the job posting. job_posted_date: The date when the job was originally posted or made public by the employer or platform. job_no_degree_mention: A flag indicating whether the job posting explicitly mentions that no degree is required (Boolean: True / False). job_health_insurance: Indicates whether the job listing includes health insurance or similar benefits (Boolean: True / False). job_country: The country in which the job is located (e.g., “USA”, “France”, “Germany”). salary_rate: The unit or frequency of the salary specified. salary_year_avg: The estimated or provided average annual salary for the job, standardized in a yearly format (numeric). salary_hour_avg: The estimated or provided average hourly wage, standardized in an hourly format (numeric). company_name: The name of the hiring company or organization offering the job. job_skills: A list or string of skills required or mentioned in the job description (e.g., “Python, SQL”). job_type_skills: A categorized or grouped skill profile, typically summarizing the type of job based on skill composition.

Migration of LinkedIn members at the country-skill level, 2015-2019 Data has been sourced from the citation “Talent Migration Data” by World Bank Group & LinkedIn Corporation, licensed under CC BY 4.0., published by The World Bank at https://datacatalog.worldbank.org/search/dataset/0038044/Talent-Migration---LinkedIn-Data- This dataset is part of the LinkedIn - World Bank Group partnership, which helps governments and researchers understand rapidly evolving labor markets with detailed and dynamic insights.

CONTENT The Dataset underlies the metrices present on the interactive dashboard of the World Bank Group-LinkedIn partnership.it cover industry, skill and migration metrices of over 100 countries. Specifically data covers 4 metrices 1.industry employment shifts 2.Talent Migration 3.Industry Skills Needed 4.Skill Penetration

There are 4 different migration specified. skill, industry and country migration .this dataset is country migration

1. Country Migration – Inter and intra country talent migration. Based on user-reported location. When a user’s updated job location is different from their former location, LinkedIn recognizes this as a physical migration. Given as the net gain or loss of members from another country divided by the average LinkedIn membership of the target (or selected) country during the time period, multiplied by 10,000.