Hiring recruits is something every company struggles with and given the fact that hundreds of candidates apply for a single post many a time, it becomes very difficult to filter out candidates to find the perfect fit. At the same time, if the process takes too long, candidates may tend to drop off. This is why algorithms have come into the picture. Job Matching Algorithms has helped job seekers in finding the best jobs to apply for. Filtering jobs and finding the ones that best fit their profile can take hours, and applying to hundreds of jobs without comparing the requirements can only end in rejections and disappointment.
Different Job Matching Algorithms
With time, computing systems have become more and more intelligent and can take independent decisions by analyzing raw data. The recruiting industry has noticed the trend and has been quick to catch on. Numerous algorithms are used to recruit candidates for open positions and we will be discussing the most common ones today. Job Matching Algorithms have helped job seekers actively filter out the requirements they need to enroll for a job role.
- Sourcing algorithms – Recruiting is a tough job, and company recruiters find it especially hard since no matter what requirements are mentioned in the job descriptions, more than fifty percent of the applicants do not go through the job description properly and apply without having the requisite qualifications. This makes recruiters spend a lot of time filtering out the serious applicants who have proper backgrounds. The sourcing algorithm does this work instead and saves companies both time and money. All you need to do is feed the software values for different parameters such as years of work experience, known programming languages, education, and more. The algorithm will go through profiles on LinkedIn or other public databases and find CVs that best fit the information provided by recruiters. Once this is done, the selected resumes are automatically sent to the recruiters so that they can do a round of manual checks and then go on to interviews. Such solutions are rising and companies like Yatedo Talent are promising to be the “Google of recruiting”.
- Filtering algorithms- Once a set number of resumes have been collected, say by the sourcing algorithm, or manually by the Talent Management team, the next step is filtering. This task too takes a lot of time when done by individuals and is much faster when using intelligent machines. In the filtering stage, the analysis goes a step above the keyword matching technique. In this stage, ML algorithms are used to analyze sentiment and semantics of text in the curriculum vitae. The goal of the algorithm here is to analyze the personality of each individual through their resumes and provide a deeper insight into each applicant. In a way, this step is used to find which candidates are best suited to the working environment of a company.
- Reverse matching algorithms- While the two previous algorithms that we mentioned are used by recruitment agencies, this one is mostly used by candidates who are looking for jobs. Platforms offering such services serve as a search engine for people who are looking for jobs. Applicants post their resume which is parsed by algorithms. The data that is extracted through parsing is then used to find jobs that match best with the candidate’s profiles. Then the candidate can go on to apply to the jobs that matched him. Matching algorithms can double up as sourcing algorithms as well. On one hand, they can provide job recommendations to applicants and on the other hand, they can send matched applicants to the companies they match with. This can help both parties while serving the company who’s providing the services with two separate revenue streams.
Limitations of Job Matching Algorithms
While job matching algorithms have made massive strides in the field of automatic job matching, there are still several issues or constraints that need to be overcome. An algorithm is supposed to be devoid of any bias, but since the training data is prepared by humans, bias often creeps into these algorithms in runtime. For example, if you train a job matching algorithm with historic data, then there’s bound to be a tendency of the algorithm to choose more men than women since the tech industry consisted mostly of men in the previous decade. Such deficiencies can creep into an algorithm when the data it is trained on is not properly analyzed beforehand.
Another problem with training job matching algorithms is that recruiters usually train algorithms with individuals who are currently on the job. This causes a definite bias in the system since the algorithm only searches for people with very similar traits and backgrounds and diverse profiles or profiles that stand out usually end up getting rejected. This, in turn, will have an impact on the diversity of applicants who end up getting hired, in terms of skills, personality traits, and experience.
When recruiters build an ideal profile for the matching algorithm to follow, candidates who have made a change in the career path or ones who have taken a long break from work may end up getting dropped. This may have a massive impact. For example, say a company sets an ideal profile of a person with no more than one month’s continuous break. Almost every woman who has taken maternity leave has more than a month’s break in their jobs. Hence, unknowingly the company will be rejecting every woman who has taken maternity leave through their filtering algorithm. On the other end of the spectrum, not every candidate who matches closely to the ideal profile would be a good fit for the company.
The most important factor to consider is that hiring is a lot about the human aspect-the emotional aspect that a machine or a computer program cannot understand automatically from resumes or even videos of individuals. This is why a part of the recruitment process must consist of human intelligence until a machine that is smart enough to measure a man’s emotional wisdom is built.
While the artificial intelligence-based systems are no close to handling recruiting on their own, they can reduce a lot of time for traditional recruiters when trained on diverse and clean training data. However, these systems should not be depended on completely so that companies get to hire profiles that stand out or deviate from the regular type of profiles. These systems should work in tandem with human recruiters to reduce the time taken by completing some of the mundane, repetitive, and basic tasks that make recruitment a tough and time taking job.