File size: 4,109 Bytes
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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.
## Files
> ### postings.csv
>
> * **job_id** : The job ID as defined by LinkedIn ([https://www.linkedin.com/jobs/view/](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.
> ### jobs/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
> ### companies/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
> ### companies/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 😁
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