Candidate Scoring Models for a Job Search Platform

by Max Frolov
CEO @ DataRoot Labs
Tech Stack:
HR tech
Project Length:
6 months
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  • While there are numerous job boards, few of them vet and score candidates based on their learning skills and actual technical skills.
  • aims to do just that. It is a niche job search portal for searching top technical talent integrated with an online & offline schools, served as a mechanism for filtering candidates.
  • DataRoot Labs built the platform from end-to-end including an AI components which filter and score the candidates.

(fig. 1) Platform:

Tech Challenge

  • The platform has a large number of students that become job candidates after finishing the online school. The main challenge lies in AI-Powered sorting students during the studying cycle to identify best talent.
  • Another challenge is to supply students with personalized study program. The program provides curated content such as customized tests and materials based on students' skills level and needs.


  • We created a series of scoring models based on the datasets and additionally publicly available data on prior students’ activity.
  • Those models evaluate candidates upon their registration for the online school, recommend the most effective studying roadmaps for a given subject.
  • Additionally, via push notifications and recommendations of additional studying material the models optimize the collaboration process to recommend the most efficient learning process.


  • The LTV of student cohorts doubled from 5% to 13%, based on the number of students who finished the school.
  • The tracking of "student-content" interaction allowed for aggregating more data and automatically enriching their internally created CVs that are later passed to potential employers.
  • As a result, students' probability to get the desired employment significantly increases. At the same time, employers get candidates that are more qualified for the position.

Updated Jul 29, 2019 — 00:00 UTC

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