Dataset

job-postings-english-clean

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The job-postings-english-clean dataset, published by 2024-mcm-everitt-ryan, is an English-language corpus of online job postings distributed as a single Parquet train split of roughly 1.76 million rows. It is tagged as US-region data and is intended for text-based machine learning workflows.

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What this dataset contains

Each row is a single job posting identified by a document_id, with metadata describing where and how it was collected. Fields such as country, locale, language, source, scraped_date, companyName and city record the origin of the listing, while dataset_id, dataset_type, dataset_license, dataset_source, dataset_title and dataset_url describe the upstream collection. The full posting text is stored in job_posting along with an HTML rendering in job_posting_html, and a categorical position_name field appears to capture the job title or role label.

Structure and fields

All textual and categorical fields are stored as strings, and scraped_date is encoded as an int64 timestamp. Identifier fields document_id, company_id and dataset_id link postings to companies and source collections, and a category column provides a coarse occupation or industry label. The schema makes the dataset straightforward to load with parquet-compatible tools such as Polars, Dask, the Hugging Face datasets library or mlcroissant.

How analysts use it

Because the corpus contains raw posting text plus standardized fields like country, city, companyName and category, it can support NLP tasks such as job-title normalization, skill extraction or occupational classification. For investment workflows, postings can be aggregated by companyName or category to track hiring intensity, geographic expansion through city and country, or industry-level labor demand trends. The size of the corpus and its English-language focus also make it suitable as pretraining or fine-tuning data for domain-specific language models.

Documentation & references 1
  1. 2024-mcm-everitt-ryan/job-postings-english-clean: 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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