The abundance of job seekers nowadays leads many companies to the use of machine learning in their recruitment process. Some examples can be found in the fields of employee recruitment, management, candidate selection, and employee management.
Talent management entails understanding the best fields, positions, and backgrounds to set talent up for success. Talent attraction selects the top outlets and channels for attracting the most suitable employees. Finally, candidate selection use natural language processing (NLP) tools on CV’s to find suitable candidates.
The demand for skilled jobs high and employees increasingly stay with company for only a few years. Due to this , companies find it increasingly important to ensure skilled employees are set up for long-term success and they turn to machine learning programs. While ML won’t displace in-person interviews, they can point to candidates with the most potential. In short, a good ML model can help companies reduce time to hire while optimizing the candidate funnel.
A good employee recruitment model incorporates as much information about successful and unsuccessful hires, past and present. It should also use job performance metrics and any information that would typically be found on the candidate’s CV. Examples include lists of skills, previous positions, responsibilities, and studies.
The key to a good recruitment model requires good natural language processing (NLP) but a great model may require a variety of external data. The most useful datasets are ones that can enrich candidate data with LinkedIn and other professional profiles for cross validation, skill extraction, and general analysis. Of course, background check data can be used to make sure that you are focusing on the right profiles if the position requires it.
Many tools can automatically extract candidates’ social media and online presences to focus on the ones with the right skill-sets, capabilities, and social demeanor. However, additional data may include competitive recruitment data and salary data to make sure the position matches salary expectations.
There are a few key challenges for the recruitment use case:
Using [Google’s AI for HR] program, Openlogix was able to search through more than 30,000 applicants to make a new hire within 24 hours. Previously, it took the firm around four weeks to hire someone. A study conducted by Delloitte found that companies spend 52 days and $4,000 to fill one open position.
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Table of INEBase Employed persons by age group, sex and Autonomous Community. Percentages with respect to the total of each Community. Quarterly. Autonomous Communities and Cities. Economically Active Population Survey
Table of INEBase Employed persons by age group, sex and economic sector, by Autonomous Community. Quarterly. Autonomous Communities and Cities. Economically Active Population Survey
Table of INEBase Employed persons by age group, sex and economic sector. Quarterly. National. Economically Active Population Survey