Occupational models from 42 million unstructured job postings

Nile Dixon, Marcelle Goggins, Ethan Ho, Mark Howison, Joe Long, Emma Northcott, Karen Shen, Carrie Yeats

Research output: Contribution to journalArticlepeer-review

Abstract

Structuring jobs into occupations is the first step for analysis tasks in many fields of research, including economics and public health, as well as for practical applications like matching job seekers to available jobs. We present a data resource, derived with natural language processing techniques from over 42 million unstructured job postings in the National Labor Exchange, that empirically models the associations between occupation codes (estimated initially by the Standardized Occupation Coding for Computer-assisted Epidemiological Research method), skill keywords, job titles, and full-text job descriptions in the United States during the years 2019 and 2021. We model the probability that a job title is associated with an occupation code and that a job description is associated with skill keywords and occupation codes. Our models are openly available in the sockit python package, which can assign occupation codes to job titles, parse skills from and assign occupation codes to job postings and resumes, and estimate occupational similarity among job postings, resumes, and occupation codes.

Original languageEnglish (US)
Article number100757
JournalPatterns
Volume4
Issue number7
DOIs
StatePublished - Jul 14 2023

Keywords

  • DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • SOC codes
  • employment services
  • job descriptions
  • job titles
  • labor markets
  • natural language processing
  • occupational hazards
  • remote work
  • skills

ASJC Scopus subject areas

  • General Decision Sciences

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