The abundance of job seekers nowadays leads many companies to use machine learning throughout their recruitment process. Some examples of this can be found in the fields of talent management, talent attraction, and candidate selection.
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.
These days, the demand for skilled jobs is very high and, as switching companies every few years has become the norm, it is essential to assure skilled employees (especially in mid–senior level positions) are set up for success. While machine learning will never remove the need for manual screening and interviews, there is a large variety of potential indicators that can help select a successful candidate to become a long-term happy employee from among potentially thousands of applicants. A good ML model can help companies reduce time to hire while optimizing the candidate funnel.
A good ML model for recruitment and candidate selection should involve as much information about current and past successful and unsuccessful hires. A good recruitment and candidate assessment model should have information that would typically be on the candidate’s CV and information about their job performance metrics. Lists of skills, previous positions, responsibilities, and studies are generally helpful.
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.
There are many tools that can automatically extract candidates’ social media and online presences to focus on the ones with the right skill-sets, capabilities, and social demeanor. 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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