LinkedIn Job Postings (2023 - 2024)

Description

Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper

Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. Each individual posting contains dozens of valuable attributes for both postings and companies, including the title, job description, salary, location, application URL, and work-types (remote, contract, etc), in addition to separate files containing the benefits, skills, and industries associated with each posting. The majority of jobs are also linked to a company, which are all listed in another csv file containing attributes such as the company description, headquarters location, and number of employees, and follower count.

With so many datapoints, the potential for exploration of this dataset is vast and includes exploring the highest compensated titles, companies, and locations; predicting salaries/benefits through NLP; and examining how industries and companies vary through their internship offerings and benefits. Future updates will permit further exploration into time-based trends, including company growth, prevalence of remote jobs, and demand of individual job titles over time.

Thank you to @zoeyyuzou for scraping an additional 100,000 jobs
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Files

job_postings.csv

• job_id: The job ID as defined by LinkedIn (https://www.linkedin.com/jobs/view/ job_id )
• company_id: Identifier for the company associated with the job posting (maps to companies.csv)
• title: Job title.
• description: Job description.
• max_salary: Maximum salary
• med_salary: Median salary
• min_salary: Minimum salary
• pay_period: Pay period for salary (Hourly, Monthly, Yearly)
• formatted_work_type: Type of work (Fulltime, Parttime, Contract)
• location: Job location
• applies: Number of applications that have been submitted
• original_listed_time: Original time the job was listed
• remote_allowed: Whether job permits remote work
• views: Number of times the job posting has been viewed
• job_posting_url: URL to the job posting on a platform
• application_url: URL where applications can be submitted
• application_type: Type of application process (offsite, complex/simple onsite)
• expiry: Expiration date or time for the job listing
• closed_time: Time to close job listing
• formatted_experience_level: Job experience level (entry, associate, executive, etc)
• skills_desc: Description detailing required skills for job
• listed_time: Time when the job was listed
• posting_domain: Domain of the website with application
• sponsored: Whether the job listing is sponsored or promoted.
• work_type: Type of work associated with the job
• currency: Currency in which the salary is provided.
• compensation_type: Type of compensation for the job.

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job_details/benefits.csv

• job_id: The job ID
• type: Type of benefit provided (401K, Medical Insurance, etc)
• inferred: Whether the benefit was explicitly tagged or inferred through text by LinkedIn

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company_details/companies.csv

• company_id: The company ID as defined by LinkedIn
• name: Company name
• description: Company description
• company_size: Company grouping based on number of employees (0 Smallest - 7 Largest)
• country: Country of company headquarters.
• state: State of company headquarters.
• city: City of company headquarters.
• zip_code: ZIP code of company's headquarters.
• address: Address of company's headquarters
• url: Link to company's LinkedIn page

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company_details/employee_counts.csv

• company_id: The company ID
• employee_count: Number of employees at company
• follower_count: Number of company followers on LinkedIn
• time_recorded: Unix time of data collection

If you find this dataset helpful, your upvote would convince me I didn't waste my summer break 😁