Data-driven Job Search Engine Using Skills and Company Attribute Filters
Rohit Muthyala, Sam Wood, Yi Jin, Yixing Qin, Hua Gao, Amit Rai

TL;DR
This paper introduces a comprehensive data-driven job search engine that enhances filtering capabilities with skills and company attributes, providing personalized and targeted job search results.
Contribution
The paper presents an end-to-end framework integrating advanced filtering options and data normalization techniques for improved job search relevance.
Findings
Enhanced filtering with skills and company attributes.
Personalized search results and networking opportunities.
Effective data normalization and ranking methods.
Abstract
According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies…
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