Dataset

GenAI-job-postings-Dataset

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GenAI-job-postings-Dataset is a small, US-focused text corpus of generative-AI and machine-learning job postings distributed in Parquet format. The dataset is published on the Hugging Face Hub under an unstated license and comprises a single train split of roughly 120 rows.

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What this dataset contains

The corpus catalogs job postings tied to generative-AI and adjacent machine-learning roles, with each record classified by seniority level and intended use case. According to the publisher, the data is drawn from US postings, although no explicit coverage window or sourcing methodology is documented. Because the license is unstated and the file size is below one thousand records, it is best treated as a research sample rather than a production feed.

Structure and fields

Each row is described by four string-valued columns: job_title, job_posting (the full posting text), position_level, and use_case. The accompanying masked examples indicate that position_level takes values such as "entry" and use_case captures functional domains such as "computer vision." The data is delivered as a Parquet file with a single train split of 120 rows, loadable via the datasets, pandas, polars, or mlcroissant libraries.

How analysts use it

Workforce and talent-flow analysts can mine the job_posting text for keyword trends, in-demand tools, or compensation language, and slice counts by job_title, position_level, or use_case to track shifts in AI hiring emphasis over time. Given the limited row count and US-only framing, it is most useful for illustrative or prototyping work within labor-market studies rather than for drawing statistically robust conclusions.

Documentation & references 1
  1. cmagganas/GenAI-job-postings-Dataset: 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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