1-3m-linkedin-jobs-and-skills-2024

asaniczka/1-3m-linkedin-jobs-and-skills-2024 is a Jobs & Workforce data product published on Kaggle and indexed by The Alternative Data Encyclopedia.

Asaniczka publishes 1-3m-linkedin-jobs-and-skills-2024 as a Kaggle offering in the Jobs & Workforce signal family. The listing has drawn 21,470 downloads and 296 likes on Kaggle.

The data

Scraped Jobs from Linkedin. Augmented with Job Skills

It is one of 94 listings in the Jobs & Workforce family; between them the practical differences come down to coverage, history depth and how the raw signal is cleaned and delivered.

The source tags it with business, education and jobs and career.

Structure, access and licensing

License: ODC Attribution License (ODC-By). Size: 2015184709. Last updated: 2024-02-08.

Access is through a dataset that is downloaded from Kaggle for use in a notebook. Its most recent recorded snapshot is from 2024-02-08.

The signal

Structured records of job postings, professional profiles and aggregated talent movement, organized by employer, role, skill and geography. Hiring is an operational decision taken ahead of results, which makes workforce data a leading indicator of corporate strategy and growth.

Hiring precedes revenue: expansion plans, product launches and retrenchment surface in postings and headcount before they reach filings. Analysts use accelerating engineering or sales hiring to flag fundamental inflections, watch layoff waves as margin events, and benchmark talent inflows between competitors. Headcount trajectories also anchor valuation work on private companies between funding rounds. Posting-level records carry title, required skills, seniority, posted salary where disclosed, location and first/last-seen dates, aggregated into hiring rates by function. Profile-derived layers estimate headcount, attrition, seniority mix and individual transitions between employers. Skills taxonomies map free-text titles onto standardized occupation and skill frameworks across markets.

Vendors crawl corporate career sites, applicant-tracking systems and job boards, deduplicate reposts and evergreen requisitions, and resolve employers to canonical entities across subsidiaries. A parallel stream parses public professional profiles into employment histories that support point-in-time headcount reconstruction. Role classification relies on curated skills taxonomies covering scores of countries; large providers report data spanning well over a hundred labor markets. Backtestability depends on recording posting open/close dates rather than storing only current snapshots.

For a first-party benchmark in this category, see TheirStack and its job-postings and buying-intents datasets, indexed on this hub with delivery, history and refresh details.

Caveats and compliance

Posting counts double-count evergreen roles and reposted positions, and shifts in board mix bias trends over time. Profile-based headcount lags reality because updates depend on self-reporting. Layoffs surface with delay, and coverage skews toward white-collar, English-language labor markets.

Postings are generally low-risk corporate content, but profile-derived data falls under GDPR personal-data rules, requiring lawful basis, aggregation and suppression of direct identifiers. Posted-salary fields need aggregation so they cannot reveal any individual's pay.

Who uses this signal

Equity analysts and quantitative funds read hiring as an early fundamental signal; VC and PE teams screen portfolio-company traction and organizational change. Macro economists consume aggregate posting demand as a real-time complement to official employment surveys.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading