Data-Driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online [34], 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, Dice and CareerBuilder. Recently, Google released their “AIpowered Jobs Search Engine”, “Google For Jobs” [30] while Facebook released “Facebook Jobs” within their platform [32]. 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 connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end “Data-driven Jobs Search Engine”. This consists of comprehensive search filters including user skill set-focused attributes and various company attributes. In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL (Extract, Transform and Load) pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users. This framework also enables deeper analytics on the job market as a whole by providing many advanced segmenting dimensions vis-a-vis the skills-based semantic data and company level attributes.