During the creation of Vettd’s backend, we processed hundreds of thousands of resumes to discover that there were approximately 159,014 unique job titles in our sample alone.
Our dedication to supporting and educating hiring managers has led us to uncover some really interesting things about this space. We’ll do our best to share our findings with you here.
During the creation of Vettd’s backend, we processed hundreds of thousands of resumes to discover that there were approximately 159,014 unique job titles in our sample alone. Of these job titles, 9,900 were used by 3 or more people. This means that professionals tasked with making hiring decisions are expected to be familiar with thousands of titles relating to their job opening. Often times, the comparable titles are made up or specific to a certain company. The problem is simple: there’s very limited standardization.
The US government and Bureau of Labor Statistics has realized this and has done a great job promoting the use of the Standard Occupational Classification (SOC) system. However, there is a huge gap between their titles and reality. The SOC is forced to group everyone into 840 detailed occupations. That means there are still roughly 9,100 additional job titles that managers have to properly interpret on their own.
With no oversight, people and companies are becoming increasingly confused about what various titles actually mean at different companies. Complicating the nomenclature makes it difficult for managers and recruiters to identify candidates for their positions. It forces anyone reviewing candidate resumes to take the time to understand each and every title variation in order to give each candidate a fighting chance.
We’re currently working on some exciting technology to combat these dynamic descriptors and will let you know when we’re ready to help interpret 159,014 titles in minutes.
You've probably read about how AI is being used in data security, financial trading, healthcare, fraud detection, or elsewhere. AI has taken hold in these industries, in part, because there is “big data” associated with each and ready-made AI solutions. But, what about the industries or use cases that don’t involve mountains of numerical data?
In the December 7th issue of First Analysis Quarterly Insights, managing director Corey Greendale concludes that there’s a significant information gap that makes it difficult for organizations to optimize internal talent mobility.
Vettd.ai college interns are gaining a unique perspective about the difference between classroom learning and real-world experience. They are being challenged to use open-source and low-cost software components to create prototype applications to compliment or compete with alternative approaches to natural language processing (NLP) solutions.
