Employability Scoring
What is it and why is it interesting for your business?
Employability scoring for recruitment involves the use of machine learning algorithms to predict the likelihood of job candidates being successfully matched with available positions.
At element61, we recognize the importance of streamlining the recruitment process to efficiently identify the most promising candidates in the HR and interim business. Drawing from a past use case with Vivaldis, a Belgian interim office, we understand the challenges of processing a high volume of job candidates without a prioritization system in place. By implementing an employability scoring model, we were able to rank candidates based on their likelihood of finding a job, allowing recruiters to focus their efforts on reviewing the most relevant resumes first.
How does it work?
In order to scan the most relevant resumes, we have built a machine learning model which predicts which candidates are most likely to be matched with a job. The employees can then scan through the most relevant resumes and as such, tackle high value candidates as fast as possible. At this point, the machine learning model is not scoring the candidates for a specific job but rather the overall employability of a candidate.
In order to define a score, we consider different drivers for the employability score such as age, language knowledge, education level, driving permit, experience, etc. Important when working with sensitive data, is that the model is built to avoid any discrimination or bias. Among others, this means we don’t include certain drivers like gender & region. The tooling developed is working hand-in-hand with Salesforce, the customer tool in place to collect and store all the information about the candidates (both information received in the offices as on the website itself). This means that Salesforce remains the end-user tool & embeds our solution.
