In the 21st century, machine learning is all around us.

It is listening to us, it is defining us, it is identifying us and it is collecting data around the world to drive insights into the future of business and our society. What used to be a relatively unexciting piece of technology (machine learning has been around since the 1980s) has evolved into a juggernaut of a technological paradigm that is working hand-in-hand with AI to make our lives easier.

 Of course, with the dawn and resurgence of new and old technologies, comes a new source of anxiety for everyday people and employees. Many wonder if AI and Machine Learning will ultimately lead to the replacement of the Staffing industry for regular employees by robots instead.

Fortunately for those of us working in recruitment; Machine Learning, and subsequently Artificial Intelligence, is set to bring a whole new level of efficiency and productivity to the world of staffing. They will help us do our job better; they won’t replace us.

An Overview of Machine Learning

Machine Learning (ML) is somewhat of a buzzword that either brings great excitement, or great confusion, depending on how much you know about it. Conceptually, Machine Learning is feeding data repetitively to a computer, so that computer will eventually be able to draw conclusions from the data it is processing. These conclusions can come in such forms as pattern-matching or identification of certain characteristics in photos.

For instance, if you take a picture of Half Dome in Yosemite and feed that photo into an Amazon Web Services application known as Rekognition, the script upon completion will present you with a tabulated list of keywords or trends that the computer noticed in the picture. With a picture of Half Dome, the conclusions would be in the form of percentages and predictions of what that picture is made up of.

It might point out that 50% of the picture is a “mountain” and 20% of the picture is a “valley” while 20% of the picture could be “evergreen trees” and finally 10% of the picture could be a “river”. AWS Rekognition is able to come to these conclusions because of the massive amount of data that is fed into it on a daily basis. Every time the Rekognition ML-algorithm sees something that looks like a mountain, it will classify it as a “mountain,” and vice versa. Thus, the machine has “learned” what a mountain is.

Utilizing Machine Learning in Recruiting

Through statistical analysis and prediction, a machine can greatly enhance a recruiter’s ability to find the right candidates for a role. One example of Machine Learning making its way into the Staffing world is the Google Cloud Jobs API. The Cloud Jobs API helps staffing firms, job search websites and recruiter’s with finding candidates through its use of an ML-algorithm to sort, predict, analyze and suggest candidates for roles based on a specified set of criteria.

Google’s algorithm matches and then ranks candidates on job titles, skills, and seniority while helping recruiters filter through industry and company-related buzzwords that can often hinder the job search process. The API collects data over time relating to the jobs that it analyzes and feeds its findings into different categories that are standardized with words based on what the role is actually doing.  

For example, you may have a company that has a position listed for an Application Systems Analyst 4, or a Systems Architect 6, with a bunch of vague words describing what the role is doing. Job seekers and recruiters often have a hard time deciphering what a position does and what skills and experience a candidate should have based on the vague wording these descriptions have. The Cloud Jobs API recognizes this fact and translates these vague words into what it believes the position is actually doing.

Thus, the ML-algorithm is able to reclassify and determine what the job is actually looking for and what the candidate would ultimately be doing. Instead of an Application Systems Analyst 4, the algorithm may classify that job as a “Data Scientist with extensive Python, Pandas and Scikit experience”. In a way, the algorithm is taking these broad and vague descriptions and translating them into a description that is more detailed and far easier to understand. The API also includes integration with other Google-based services like Geolocation to determine proximity and location for job seekers to job search results.

Dice and Careerbuilder are both utilizing Google Cloud Jobs API for enhancement of the job search process. Proof of its power: Careerbuilder found that after they integrated the API into their framework, within 48 hours they were able to build a prototype that delivered improved search results that were more accurate in comparison to its existing algorithm. The CEO of Jibe, Joe Essenfeld, even proclaimed that With the launch of Google Cloud Jobs API, Google machine learning will become the standard for career-based websites.So far, the Google Cloud Jobs API is able to accurately classify information based on 30 general job categories, over 1000 occupational families and over 250,000 specific job titles.

Where does the Staffing Industry go from here?

With the inroads machine learning is now making into the staffing industry, we are expecting to see a big increase in investment for machine learning based work models and algorithms. There is even a chance for the rise of one, widely adopted recruitment platform or Applicant Tracking System that is optimized for machine learning. At the core of this new platform, automation will be a key factor in its operation. With current technology, many platforms still require the recruiters to do a lot of heavy lifting, such as reading through resumes, sourcing, screening and matching candidates to the right roles as well as scheduling interviews with hiring managers.

In the near future, this may shift to an automation-based workflow integrated with machine learning algorithms which help improve the efficiency of this sourcing process, essentially eliminating the most tedious and frustrating aspects of current Applicant Tracking Systems. Recruiters can breathe easy as these algorithms are not designed to replace them; they are simply designed to assist in the more tedious parts of their jobs. While the machine does the heavy lifting, recruiters will be free to focus on the more productive and fulfilling aspects of their work such as building strong relationships with both clients and candidates.

Machine Learning is a fascinating and ever-growing field that is set to play a major part in the future of recruitment. With full integration into a company’s workplace, machine learning is capable of freeing up huge amounts of time for recruiters by learning how to sort through and manage the most repetitive and tedious aspects of the staffing industry.  In the future, we certainly expect to see more and more machine learning integrating itself into our everyday work lives.

Are you a Machine Learning Engineer? Interested in working with some of Fintech and the Tech industries hottest companies? Send us your resume and we would love to work with you!


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