Dataset

tech-job-postings-labeled

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tech-job-postings-labeled is a small public dataset of labeled technology job postings published on Hugging Face by SuhaibAtef. It comprises roughly 8,000 records tagged with source labels and is distributed in JSON.

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What this dataset contains

Each row represents a single technology-related job posting collected from public sources such as LinkedIn, with the source of the listing recorded alongside the posting itself. The dataset is tagged as US-focused and is sized between 1,000 and 10,000 rows, making it a compact corpus for text-classification experiments rather than a comprehensive labor-market feed. It is available under an unstated license and has seen limited download activity on the platform.

Structure and fields

The schema is text-only and JSON-formatted, with eight string-typed columns: id, title, company, description, source, location, url, and posting_date. The id field serves as a unique posting identifier, while source tracks the collection channel (for example, lukebarousse_data_jobs or all_tech_jobs) and posting_date holds the listing date. Splits for train, validation, and test are provided across the full 8,000-row corpus.

How analysts use it

Because the file is small, freely available, and already split, it is best suited to NLP prototyping, such as training classifiers that categorize postings by title, company, or source. For investment-fund workflows, it could serve as a teaching example for building skills taxonomies or job-text embeddings, rather than as a primary labor-market signal. Its limited size and unstated licensing terms argue against treating it as a production dataset.

Documentation & references 1
  1. SuhaibAtef/tech-job-postings-labeled: listing on Hugging Face

Further reading

All guides
  • What is alternative data?Alternative data in investment research: what the term covers, how it differs from data format, and why provenance, timing, access and validation matter.
  • Job postings as an investment signalWhy hiring data predicts fundamentals: growth intent, expansion geography, technology adoption and cost control, how postings are collected, structured and turned into tradeable indicators.
  • Web scraping and compliance in alternative dataHow scraped datasets are actually collected, where terms of service and computer-muse laws draw lines, what hiQ v. LinkedIn settled and did not, and the diligence checklist for legally fragile data.
  • Free alternative data sourcesThe practical list of free alternative-data sources for investors: public filings, official statistics, satellite and earth observation, on-chain data, web archives and open registries, what each covers, what it costs in engineering effort, and where free data falls short.
  • Alternative data for investingHow funds turn alternative data into an investment edge: signal families, evaluation, backtesting, compliance and the build-vs-buy decision, mapped to the datasets and providers indexed on this encyclopedia.
  • Job market data: datasets and providersThe datasets and providers behind labor-market signals: job postings, payroll, employee profiles and job-change data, what each source measures, its caveats and how to choose one.

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