Is Machine Learning for Candidate Searches a Good Solution?
Job searching today can be stressful on the candidate and recruiting side. On the candidate side of things, one has to worry about carefully matching their skills, professional experience, and demeanor when applying for a new position. Many times a candidate has to take some risk when applying for a new job, either in their covering letter or how they approach an employee at the prospective company. The candidate needs to appear professional, but calm. Relaxed, but ready for the challenge.
This is not an easy task. It means trying to gage exactly what the recruiter is looking for while trying to have an edge on everyone else in the candidate pool. On the recruiting side of things, recruiters and HR departments are flooded with applications from candidates that are domestically and internationally based, that come from wide disciplines, and many of whom have very similar experience.
Sometimes candidates with good enough job experience are not considered for roles because of the size of the applicant pool. Sometimes, recruiters need to make tough decisions when they are urgently hiring and don’t have too much time to delay before making a final decision on someone. This whole process, which can be slightly daunting, and often requires a little bit of “luck” on the candidate side, does demand some help on the recruiting side of things.
Enter AI and Machine Learning
On both sides of the equation, it is important to recognize that hiring costs money. According to the Society for Human Resource Management, in 2018, the average cost per hire is US$4,129, and it takes an average of 42 days to fill an open requisition. This calls for some help on the recruiting side to speed things up and make hiring a more efficient process.
Artificial Intelligence and Machine learning specifically is helping on this front, doing the work of humans when it comes to looking for keywords in resumes at a much faster pace. According to Oracle, machine learning:
“iteratively applies algorithmic analytical models to preprocessed data to uncover hidden patterns or trends that can be used to flag ideal résumés to review, predict the correct response to inquiries in the pre-engagement…”
This is important when it comes to helping candidates hear back faster from recruiters and potential employers, as days spent idling by wondering what has become of an application also means days, and money potentially lost looking elsewhere.
A Double Edged Sword
The real interesting part about machine learning is of course whether or not AI finds the same subtle criteria that is desired by employers further down the line. For example, it might be great if a machine learning program helps reduce a candidate list from 100 applicants to 25, but is it ethical for the Machine Learning to then go through the next round of candidates or should that job be exclusively reserved for humans?
Similarly, it needs to be considered if the obvious keyword search being used for an algorithm are really a test for the right candidate. In other words, candidates everywhere will be able to match the language of the job description and tweak their resumes to the employers liking. There needs to be an extra layer of depth, the human layer, that then goes beyond this to see if the candidate really matches up for the job. This second or third layer should search for things that machine learning simply can’t, such as an extracurricular activity that might not be picked up by the algorithm, but demonstrates a valuable skill or learning experience that is an asset for the organizational role in question.
So in terms of saving money and allocating funds for recruitment in strategic areas, like Machine Learning with respect to social media platforms—the new era of hiring is doing a good job. It has become a more efficient process in terms of first round screenings and cutting the stack of applicants in half. But it is also a double edged sword when the system works against itself by creating silos for recruitment, as could very well be the case if the right candidate was simply not on LinkedIn (rare, but still possible).
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Job searching today can be stressful on the candidate and recruiting side. On the candidate side of things, one has to worry about carefully matching their skills, professional experience, and demeanor when applying for a new position. Many times a candidate has to take some risk when applying for a new job, either in their covering letter or how they approach an employee at the prospective company. The candidate needs to appear professional, but calm. Relaxed, but ready for the challenge.
This is not an easy task. It means trying to gage exactly what the recruiter is looking for while trying to have an edge on everyone else in the candidate pool. On the recruiting side of things, recruiters and HR departments are flooded with applications from candidates that are domestically and internationally based, that come from wide disciplines, and many of whom have very similar experience.
Sometimes candidates with good enough job experience are not considered for roles because of the size of the applicant pool. Sometimes, recruiters need to make tough decisions when they are urgently hiring and don’t have too much time to delay before making a final decision on someone. This whole process, which can be slightly daunting, and often requires a little bit of “luck” on the candidate side, does demand some help on the recruiting side of things.
Enter AI and Machine Learning
On both sides of the equation, it is important to recognize that hiring costs money. According to the Society for Human Resource Management, in 2018, the average cost per hire is US$4,129, and it takes an average of 42 days to fill an open requisition. This calls for some help on the recruiting side to speed things up and make hiring a more efficient process.
Artificial Intelligence and Machine learning specifically is helping on this front, doing the work of humans when it comes to looking for keywords in resumes at a much faster pace. According to Oracle, machine learning:
“iteratively applies algorithmic analytical models to preprocessed data to uncover hidden patterns or trends that can be used to flag ideal résumés to review, predict the correct response to inquiries in the pre-engagement…”
This is important when it comes to helping candidates hear back faster from recruiters and potential employers, as days spent idling by wondering what has become of an application also means days, and money potentially lost looking elsewhere.
A Double Edged Sword
The real interesting part about machine learning is of course whether or not AI finds the same subtle criteria that is desired by employers further down the line. For example, it might be great if a machine learning program helps reduce a candidate list from 100 applicants to 25, but is it ethical for the Machine Learning to then go through the next round of candidates or should that job be exclusively reserved for humans?
Similarly, it needs to be considered if the obvious keyword search being used for an algorithm are really a test for the right candidate. In other words, candidates everywhere will be able to match the language of the job description and tweak their resumes to the employers liking. There needs to be an extra layer of depth, the human layer, that then goes beyond this to see if the candidate really matches up for the job. This second or third layer should search for things that machine learning simply can’t, such as an extracurricular activity that might not be picked up by the algorithm, but demonstrates a valuable skill or learning experience that is an asset for the organizational role in question.
So in terms of saving money and allocating funds for recruitment in strategic areas, like Machine Learning with respect to social media platforms—the new era of hiring is doing a good job. It has become a more efficient process in terms of first round screenings and cutting the stack of applicants in half. But it is also a double edged sword when the system works against itself by creating silos for recruitment, as could very well be the case if the right candidate was simply not on LinkedIn (rare, but still possible).