Candidate Scoring Models for a Job Search Platform

Searching top technical talent within an online school.

DRL Team

AI R&D Center

29 Jul 2019

5 min read

Candidate Scoring Models for a Job Search Platform

Client Services

AI Solutions Development Startup Venture Services

Industries

HR Tech

Next Case

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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

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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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