Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies.
Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process. This technology simply takes information contained in a job posting and from a candidate’s resume and – based on previously defined requirements – sifts through it to create the best possible matches.
Tomorrow, machine learning based hiring algorithms will go beyond simpler filtering. They will “learn” employer preferences just like dating algorithms learn preferences. This approach can be used to automatically match candidates based on job description, industry, relevance, skills, and experience. Best of all, this can be done accurately, for hundreds of candidates at a time.
Considering that during the initial resume screening only the top 2% of candidates make it to the interview, there can be many resumes to sift through, hence the importance of utilizing job matching algorithms. These tools help fill positions faster, as the search is quick and simple and the candidates that are ultimately more qualified will be auto-ranked and presented to the company.
Machine learning is great for job seekers too, as it helps them to instantly find the most relevant jobs that fit their skills and experience. On average, it takes 23 days to go through the standard interview process, 1.3x longer than the average just four years ago – so machine matching speeds up the process for everyone. With better job matching capabilities, machine learning can make picking out the appropriate opportunities much easier as well as minimize wasted time for both candidate and employer.
Now let’s go a bit deeper into the differences between machine matching and manual matching to demonstrate why the former can be more efficient. In manual matching, recruiters have to classify and allocate specific skillsets to individual candidates which can be a lengthy and arduous process. Furthermore, manual matching requires utilizing keywords to select candidates while with machine learning, keywords are not a necessary decision tool. When machine matching occurs, it looks past classifications and analyzes key skills and experience holistically. Instead of matching candidates and companies based on job titles, which are often inaccurate, it matches based on a wide variety of features, providing a better potential match in terms of capability.
With all the benefits of machine matching, companies will be able to quickly and efficiently sort through candidates. Later on they can add additional data into the program to help further screen potential employees. Companies such as Uber now utilize quantitative based assessments in the hiring process, which helps them further improve the quality of their matches. These types of tests ensure that candidates will be a strong fit for the company, which then results in less employee turnover, increased productivity, and increased hiring transparency. In Uber’s case, it dramatically improved the quality of hires and therefore lowered the pressures of managing a high-growth startup.
Matching candidates and employers is an age old process that has evolved into a much more complex system thanks to advancements in technology. With so many new variables in the hiring process – especially for technical jobs – machine learning can significantly help improve the process of finding the right candidates for the right position, making the best possible long-term match.
Contributed by: Sham Mustafa is the co-founder & CEO of Correlation One, a talent platform focused exclusively on data scientists. Prior to launching Correlation One, Sham directed Operations at two specialty finance firms. Mustafa has also provided business advisory services to more than 600 small businesses. He holds an MBA and MPA from Columbia University and a BA & LLB from the University of Madras, India.
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Interesting article. Aspiring Minds has developed the largest automated assessment based recruitment ecosytem to help bridge the gap between employers and the most relevant job seekers. Machine Learning is playing an instrumental role in the defining growth of several companies. Read more here: http://research.aspiringminds.com/