👉🏽 At the beginning of my career, I worked for Pilat, a company that supplied HR services and psychological testing services. We used bag or words statistical techniques to match job description to applicants resumes. It was better than nothing.
👉🏽 To AI or not to be, I don’t think it changed much for the better.
👉🏽 Most ATS don’t expose the algorithms behind filtering and matching candidates to jobs, but MIT technology review exposes some of the logic behind it, like: Profile information, Keyword matching, Behavioral data like interaction with job posts, availability signals i.e “open to work” and location.
👉🏽 What I think is missing and is especially relevant for LinkedIn environment, is matching between the candidates and the organizations, especially because LinkedIn “knows” a lot about the organization that is recruiting and the employees that are in it. A metric that will calculate the distance between the core personal data on the candidate and the core data about the job as it relates to other employees in the organization, might do a better job.
👉🏽 We have all the tools now with dimensionality reduction, LLMs and more to increase accuracy, remove much of the load from recruiters and reduce type II mistakes, missing good candidates.
