Candidate Scoring Models for a Job Search Platform

Searching top technical talent within an online school.

Yuliya Sychikova
COO @ DataRoot Labs
29 Jul 2019
5 min read
Candidate Scoring Models for a Job Search Platform

Summary

  • While there are numerous job boards, few of them vet and score candidates based on their learning skills and actual technical skills.
  • dhired.com 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.
4
Months
4
Engineers
6+
Technologies

Tech Stack

Django
Gensim
PostgreSQL
Python
TensorFlow
NLTK

Timeline

4 weeks
Slack Bot Development
Backend Developer
5 weeks
Data Science Roadmap Preparation
Deep Learning Researcher
6 weeks
Web Platform Development
Backend Developer
Frontend Developer
5 weeks
Matching System Development
Deep Learning Researcher

Tech Challenge

  • The platform has a large number of students that become job candidates after finishing the online school. The main challenge lies in 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.

Solution

  • 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.

Impact

  • The LTV of student cohorts doubled from 5% to 10%, 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.

Author

Yuliya Sychikova
COO @ DataRoot Labs
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.

Co-Authors

Ivan Didur
CTO @ DataRoot Labs
offices map
Kyiv (HQ)
Max Frolov
CEO @ DataRoot Labs
Tel Aviv
Ivan Didur
CTO @ DataRoot Labs
Los Angeles
Yuliya Sychikova
COO @ DataRoot Labs
builds and implement AI-powered systems across different verticals to help our clients operate effectively.
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