LinkedIn Data Jobs Dataset

Scraped LinkedIn job postings for data-related roles (Data Analyst, Data Engineer, Data Scientist, etc.)

Overview

This dataset contains job postings scraped from LinkedIn, including job titles, companies, locations, descriptions, and job types (remote/hybrid/onsite). The data can be used for data cleaning, NLP analysis, skill extraction, and building AI-powered job application tools. ## Dataset Features Column Name Description Title Job title (e.g., "Data Analyst," "Product Analyst") Company Hiring company name Location Job location (city/country) Description Full job description (may include company info) Job Type Remote, Hybrid, or Onsite (if available)

Potential Use Cases

✅ Data Cleaning & Normalization – Standardize job titles, locations, and descriptions. ✅ NLP & Skill Extraction – Find the most in-demand skills (Python, SQL, ML, etc.). ✅ Job Type Analysis – Compare remote vs. onsite job trends. ✅ AI-Powered Job Tools – Build a Streamlit app to generate:

"About Me" sections tailored to job descriptions.

Auto-generated cover letters based on job requirements.

GitHub Collaboration Want to contribute? Join the project here: 🔗 https://github.com/JoyKimaiyo/Web-scraping-data-jobs-and-automating-about-me-section

Acknowledgments Data scraped from LinkedIn for educational/non-commercial use.

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.

This dataset contains a collection of software engineering job listings scraped from LinkedIn. It provides valuable insights into the current job market, job requirements, and company hiring trends.

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 software engineering job titles.
2. Explore the geographical distribution of software engineering job opportunities.
3. Identify the most sought-after programming languages and skills in job descriptions.
4. Determine the average years of experience required for different job levels.

Photo by Hack Capital on Unsplash

The following dataset is extracted using an API and contains job data from LinkedIN about 10 common job roles in Inida.

The data can be used for a comprehensive analysis on application trends, peak posting times, popular job titles, company dynamics, geographical patterns, sector-specific insights, job freshness, company-specific behaviors, and predictive modeling. Predictive modeling can be used to anticipate job market dynamics. In summary, this dataset facilitates a thorough exploration of the job market, providing actionable insights for both job seekers and employers.

Dataset Columns

id: Unique identifier for each job posting (Integer).

publishedAt: Date when the job was published (String, formatted as 'YYYY-MM-DD').

title: Job title (String).

companyName: Name of the hiring company (String).

postedTime: Time since the job was posted (String).

applicationsCount: Number of job applications received (Float).

description: Job description, including required skills (String).

contractType: Type of employment contract (String).

experienceLevel: Level of experience required for the job (String).

workType: Type of work arrangement (String).

sector: Industry sector of the job (String).

companyId: Unique identifier for the hiring company (Integer).

city: City where the job is located (String).

state: State where the job is located (String).

recently_posted_jobs: Indicates whether the job is recently posted (String, 'Yes' or 'No').

Dataset

This dataset was created by RATNESH SATYARTHI

Released under Apache 2.0

Contents

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.

Dataset

This dataset was created by Muhammad Sufyan

Released under CC0: Public Domain

Contents

The dataset contains information on 30,000+ job postings collected from LinkedIn till the year 2023 which provides a rich source of information on job postings on LinkedIn, with concise information on the job title, company, location, and other key attributes of each posting. This data can be used to gain insights into employment trends and dynamics, identify key skills and experiences that are in high demand, and optimize job postings to attract the right candidates. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18065122%2Ff51a9373d9b0bbc78235ae4be8dbdce5%2F1.jpeg?generation=1751200071553062&alt=media" alt="">

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.

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

Merged files from https://www.kaggle.com/datasets/asaniczka/1-3m-linkedin-jobs-and-skills-2024

This comprehensive dataset contains a curated collection of 100 job postings for Data Analyst positions, sourced from LinkedIn. As the demand for skilled data analysts continues to surge, this dataset serves as a valuable resource for data enthusiasts, aspiring data analysts, and researchers alike.

LinkedIn Job Postings Dataset

Description

This dataset contains information about job postings on LinkedIn. The data is divided into several files, each containing different aspects of the job postings:

1. job_postings.csv: This file contains detailed information about each job posting, including the job title, description, salary, work type, location, and more.
2. companies.csv: This file contains detailed information about each company that posted a job, including the company name, website, description, size, location, and more.
3. company_industries.csv: This file contains the industries associated with each company.
4. company_specialities.csv: This file contains the specialties associated with each company.
5. employee_counts.csv: This file contains the employee and follower counts for each company.
6. benefits.csv: This file contains the benefits associated with each job.
7. job_industries.csv: This file contains the industries associated with each job.
8. job_skills.csv: This file contains the skills associated with each job.

Usage

This dataset can be used for various purposes such as: - Analyzing the job market - Analyzing company trends - Analyzing salary trends - Building a job recommendation system - Natural Language Processing (NLP) tasks such as keyword extraction, topic modeling, etc.

Acknowledgements

This dataset was collected from LinkedIn. Please note that the data may be subject to LinkedIn's terms of use.

License

This dataset is released under the Open Database License (ODbL).

Dataset

This dataset was created by Steve Marcello Liem

Contents

The data comprises job-related information from LinkedIn job postings scraped over a 2-day period. Key features include company details and job-specific information like title, description, and salary. The dataset provides a comprehensive view for exploring factors influencing job posting characteristics and has been reformatted from its original source to improve its compatibility among various machine learning algorithms.

Collated 8 different data source. Filtered for only Data and ML jobs, titles and descriptions. Applied text data cleaning and preprocessing, documented here: https://tianyimasf.github.io/ai/data-cleaning/.

1. LinkedIn-Tech-Job-Data: A compilation of job posts and metadata scraped from various tech categories on LinkedIn

2. Data Analyst Jobs: This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions

3. US Job Postings from 2023-05-05: This dataset is an excerpt of our web scraping activities at Techmap.io and contains a sample of 33k Job Postings from the USA on May 5th 2023.

4. LinkedIn Job Postings Dataset: This dataset contains information about job postings on LinkedIn.

5. LinkedIn Job Postings - Machine Learning Data Set: The data comprises job-related information from LinkedIn job postings scraped over a 2-day period.

6. Linkedin Canada: Data Science Jobs 2024: The "LinkedIn Canada: Data Science Jobs 2024" dataset presents an insightful overview of the data science job market in Canada as sourced from LinkedIn.

7. Data Scientist - Linkedin Job Postings: This dataset provides valuable insights into data science job postings, including the required skills and software proficiency sought by employers.

8. LinkedIn Job Postings Dataset: This dataset contains information about job postings on LinkedIn.

Initially used for my project analyzing data job market, including analyzing titles, skills, and company functions. Could be used for other purposes like posting generation.

Dataset

This dataset was created by davideev9

Contents

LinkedIn Jobs Dataset

Structured job listings collected from LinkedIn using Apify. Designed as a sample for analytics, machine learning, and recruitment intelligence.

Dataset Overview

Each row includes:

• jobTitle – Role title
• companyName – Hiring company
• location – Job location
• employmentType – Full-time, Part-time, Contract, etc.
• experienceLevel – Entry, Mid, Senior
• salary / compensation – Salary data
• jobDescription – Full text description
• skills / keywords – Extracted skills
• postedDate – Posting date
• applicants / insights – Engagement metrics
• jobUrl – Direct link
• jobId / referenceId – Unique identifier

CSV, Excel, and JSON compatible.

Data Collection

Generated using Apify's Fast LinkedIn Job Scraper.

Pipeline details:

• Bulk extraction
• Data normalization
• Duplicate filtering
• Location, keyword, and job-type filters
• Batch or real-time delivery

Use Cases

• Job market trend analysis
• Skill demand tracking with NLP
• Salary benchmarking
• Job recommendation systems
• Recruitment intelligence
• AI/ML models for career data

Custom Datasets

Available custom data options:

• Datasets with millions of listings
• Country or industry filters
• Historical and real-time feeds
• Daily or hourly scheduled scraping
• API delivery
• Custom skill extraction and categorization

Contact

Shahid Irfan

Apify Actor Builder specializing in web scraping, data pipelines, and API integrations.

Profile: apify.com/shahidirfan

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.

The jobs_linkedin.csv file comprises the scraping results obtained from LinkedIn website. It includes the following columns: 1. title: Signifies the job title associated with each entry. 2. location: Provides information about the job's location. 3. time: Indicates the timestamp when the job post was uploaded. 4. link: Contains a unique identifier (UUID) and a direct link to the respective job post. 5. desc: Contains the comprehensive description of each job opportunity.

For a more detailed exploration of my NLP work, please refer to: - LinkedIn-NLP-Notebook - LinkedIn-NLP&DL-Notebook