Hirecurve & goML worked together to build an entity detection & recommendation model to improve the jobs recommendation for the applicants, thereby increasing the customer base by 11% in 6 months.

Table of Contents

Business Problem

HireCurve’s value proposition is that their job skills match 80% accuracy for the candidate profile. While they were able to deliver this to a certain extent, most of this work was manual & supported by a simple keyword-based classification engine
While this worked when they received 50 applications a day, they started facing challenges when this number went up to 500 applications and more per day
goML worked with the HireCurve team to build a multi-entity classification engine to map the right skillsets from the candidate profile (structure) & resume (unstructured) data to the suitable job openings

We worked with the team at HireCurve to build the data from 2 sources
S3 Dump of Resume Files
Application API(REST) to read structured data that was captured during profile creation
We then performed text cleaning and keyword selection according to hiring criteria, leveraging Python on Amazon SageMaker
Post the keyword selection, a cluster analysis to identify a keyword category that relates to a particular job description was, made followed by feature engineering using the frequency of keywords and n-gram
We then encoded n-grams and used PCA to simplify the data-set
Eventually, using word2vec and neural networks, the resumes were classified into different job groups to identify a relevant job opening leveraging Python, RNN, Pandas, Scikit learn on Amazon SageMaker
Finally, the RNN model was deployed for inferencing Amazon SageMaker
The output was published as an API endpoint to be consumed for the job(s) recommendation