This document lays out the input and output schema for the full list of Transformations provided in the Catalog. Note that for every single output, Refuel will also produce a confidnence score.

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Resume Parsing

Input:

  • resume_link (str): Either a publicly readable URL, or a path to S3 or GCS that can be read by Refuel through our integration.

Output:

  • candidate_name (str): Name of the candidate
  • contact_info (json): A JSON object containing any physical addresses, email addresses, phone numbers or web addresses (LinkedIn, Github, personal websites, etc) for the candidate
  • education (list(json)): A list of JSON objects, where each JSON contains the school, major, degree, start year, end year and other information about a specific educational degree for the candidate
  • work_history (list(json)): A list of JSON objects, where each JSON contains the job title, company, start month, start year, end month, end year and description about a specific job held by the candidate
  • skills (str): List of skills demonstrated by the candidate based on evidence in their resume.

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Skills Extraction and Mapping

Input:

  • link (str): Either a publicly readable URL, or a path to S3 or GCS for a resume, job description or other document from which skills needs to be extracted.

Output:

  • skills (str): List of skills demonstrated by the candidate based on evidence in their resume, and mapped against a taxonomy.

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Job Title Normalization

Input:

  • title (str): Job title to be normalized

Output:

  • normalized_title (str): Job title as a string, with typos corrected, short forms expanded (ex. Sr to Senior), unnecessary modifiers or adjectives removed

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Job Title Seniority Classification

Input:

  • title (str): Job title

Output:

  • seniority (str): The job title will be categorized against the following taxonomy:
  1. Owner
  2. Founder
  3. C Suite
  4. Partner
  5. VP
  6. Head
  7. Director
  8. Manager
  9. Senior
  10. Entry
  11. Intern
Introduction