Candidate Scoring Models for a Job Search Platform
Searching top technical talent within an online school.
AI R&D Center
29 Jul 2019
5 min read
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.
Tech Stack
Django
Gensim
PostgreSQL
Python
TensorFlow
NLTK
Next Case
Call Center Optimization for a Medical Tourism Marketplace
Identifying the psychotype of a prospect.
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.
Have an idea? Let's discuss!
Book a meeting
Yuliya Sychikova
COO @ DataRoot Labs
Do you have questions related to your AI-Powered project?
Talk to Yuliya. She will make sure that all is covered. Don't waste time on googling - get all answers from relevant expert in under one hour.
Talk to Yuliya. She will make sure that all is covered. Don't waste time on googling - get all answers from relevant expert in under one hour.
OR
Important copyright notice © DataRoot Labs and datarootlabs.com, 2025. Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to DataRoot Labs and datarootlabs.com with appropriate and specific direction to the original content.
Author
AI R&D Center
Our team shares experiences and insights on how AI and ML change and shape new markets, optimize various industries and our lives.
Co-Authors
CTO @ DataRoot Labs