The Challenge

Our mother company, Algorhythm Group, assigns its consultants to new opportunities at clients. The responsibility of the business unit managers is to screen job offers and CVs to find an ideal match for consultants in their unit. ​

As both Algorhythm Group and the amount of available offers grow in size, it becomes increasingly more challenging to manually screen and match hundreds of CVs to new job applications. ​

The business unit managers would rather spend their time doing more meaningful tasks than manually go through these documents.

The Solution

Our team set up a data architecture on Azure to collect, clean and process job offer data and CV data of consultants. ​

Using Natural Language Processing and Artificial Intelligence, we trained a recommendation model to match job offers to consultants according to their CVs and business unit (domain of expertise). The model was trained and served to end users on Azure Machine Learning. We set up continuous monitoring to track performance metrics and retrain the model in production accordingly.

The Result

By automating the screening process, business unit managers instantly receive job offers that are matched to candidate consultants in their unit. They no longer have to go through endless documents and manually screen/match consultants and offers. ​

This frees up time for the managers who can now focus on more important tasks like sourcing, negotiation, and onboarding.​