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.
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Yuliya Sychikova
COO @ DataRoot Labs
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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