The US based recruitment firm was struggling with low conversion rates on the submitted resumes. They had already identified a few reasons – limited competence of the recruiter to assess candidates on different skills including emerging tech, the slow process of manually skimming through thousand of resumes and difficulty of recruiters to identify and link different technologies in the job order. They were looking for an AI assisted solution that could address all these.
Better outcomes with lesser resources
The artificial intelligence (AI) algorithm is able to standardize the process, reducing the time to identify the right matches and enable recruiters to focus on the recruitment process itself.
The turn around time (TAT) for candidate matching process reduced by 90%
Accuracy on technical roles as high as 80%
Processed those jobs which were earlier set aside due to resource constraints
The solution was deployed as a platform to extract job orders from client links and then use the candidate database to find the matching candidates.
Manual and broken recruitment process
The recruitment process was very manual and broke. As a job order was published on a client site, an automated email would be sent to the registered mail. The person may or may not look at the job order immediately. Once the job order was received, the task would be assigned to a relevant team. Multiple recruiters would then work on the job order, after assessment of the job order details. This would often lead to shortlisting the same candidates.
The recruiters are not equipped to understand the job order requirements well. They are not conversant in different technologies to connect the dots and identify similar skills or progression of skills. Additionally, the technical details in the resumes is lost on recruiters. All these lead to an efficient process where recruiters would recommend candidates that are not fit for a role.
Machine Learning driven speed and accuracy
Artificial Intelligence based job order to candidate matching offers advantages of turn around time and better match results.
We used natural language based skill extraction from job orders, along with job requirements like location, travel required, domain expertise required.
The resumes were processed to extract skills like number of years on a specific skill or a role, the technology and domain expertise and other relevant details.
Finally, a scoring algorithm was developed to match job orders against all the candidate resumes to find the candidates best matching a job order.
A web based interface enabled recruiters to focus on prospects and assess soft skills and willingness to join.