These docs are for v2.0. Click to read the latest docs for v3.0.

Resume & Job Description Parser

Resume Parser

Affinda's Resume Parser is the core product within the Recruitment Product Suite.

Data extracted

Affinda can return over 100 data fields from resumes. These data fields include:

Personal details

Title, first name, middle name, last name, address, contact phone, email, websites, date of birth, headshot, LinkedIn profile

Work experience

Employer, job title, location, dates employed, total years experience, occupation classification, management level

Education

Institution, degree, degree type, accreditation, year graduated

Certifications

Courses, diplomas, certificates, security clearance, and more

Skills

Individual skills (mapped to a detailed best-in-class taxonomy containing over 3,000 soft and hard skills), skill type, number of months using skills

Language(s)

Language(s) spoken, the language of the resume

Summary

Candidate summary and objective, section raw text, the probability the document is a resume

Referee details

Name, phone number

HR-XML

The data that Affinda extracts from resumes can be exported in HR-XML.

By default, our resume parser will return the data that our AI model has extracted in Affinda's own standard schema. However, users can specify that they wish to return data in HR-XML format via the API by specifying it in HR-XML by specifying within the API GET request.

Job Description Parser

Affinda's Job Description Parser uses the same technology as the Resume Parser to deliver similar data and similarly high accuracy.

Typically, the Job Description Parser is used in conjunction with the Search & Match solution to match candidates to jobs, however, it can be used standalone.

Data extracted

  • Job Title
  • Occupation Classification
  • Management Level
  • Years Experience Required
  • Organisation
  • Location
  • Job Contact Details (Name, Phone Number)
  • Start Date
  • Job Type (e.g. Full Time, Part Time, Casual)
  • Languages Required
  • Skills (mapped to a detailed best-in-class taxonomy containing over 3,000 soft and hard skills)
  • Education Level Required
  • Education Accreditation
  • Certifications
  • Remuneration