Finding the right talent is a headache for hiring managers. With so many job applications to sift through, the manual process often overlooks deserving candidates and in today’s ultra-competitive job market, is not a very reliable or efficient mode of hiring.
A plethora of AI powered startups have sprung up recently offering solutions to hiring companies. Out of these, a company called Uncommon, aims to filter candidates who come from diverse backgrounds and reduces human bias.
Uncommon has used machine learning to build it’s unique candidate filtering model. Their ML model has been trained on over 50 million resumes and over 6 million job descriptions to predict and identify the most deserving and qualified candidates. Uncommon’s founder, Amir Ashkenazi, claims that the model can make predictions with 95% accuracy, a rate far better than any human can manage.
The company claims that for each job offering, it will only recommend 100% interested and qualified candidates. The process works as below:
- Once the job is listed, Uncommon’s engine will source active and passive candidates across the internet
- Based on the custom job requirements specified by the hiring company, Uncommon’s tech will analyze education, experience, and a mix of tech and soft skills to identify candidates
- The selected candidates can then be compared using Uncommon’s visualization feature
Uncommon has so far partnered with brands such as Amazon, Google, Lyft, among others.
Check out how Uncommon’s process works in the below video:
https://www.youtube.com/watch?v=_hzh08DotQE
Our take on this
This is quite a promising entry into a very competitive market. The fact that it can reduce a human’s unconscious bias is a major selling point for the company. The company is working on a Cost-Per-Interested and Qualified model, which means you only pay for qualified applicants. It seems to be a good strategy but it remains to be seen if they can distinguish themselves from the herd in the near future.
Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.
