Can Machine Learning Help Find the Perfect Fit?

Finding the perfect candidate for a role involves time, effort, and resources to sift through the pile of resumes, set up interviews, and evaluate potential candidates. As such, the hiring process has always been a laborious task for companies, who then go on to face significant risks should it result in a bad hire.

That is, until the development of machine learning for predictive hiring came along.

The benefits of predictive hiring are far-reaching in finding the perfect fit. Whether you’re a potential hire, a recruiter, or a business owner, understanding how this tech works, how it benefits the recruiting process, and its implications are essential to success.

What is Machine Learning?

Machine learning is a type of artificial intelligence that uses algorithms to learn, make predictions, and draw connections from large amounts of data at a fast pace. These computer systems don’t require explicit instructions to draw inferences. This facilitates an automated learning process where the work is performed by machines rather than humans. This frees up time to do more important work.

In the hiring process, the application of machine learning is wide-reaching. Machine learning algorithms help companies find the ideal fit for a role by analyzing vast data sources such as social media profiles, resumes, and other such available information online. The technology helps managers to make better and more informed decisions so that onboarding talent becomes easier and more efficient.

What Is Predictive Hiring?

Predictive hiring refers to the process of using machine learning algorithms to predict the eligibility of a candidate based on different data points. It’s a recruiting technology that analyzes large amounts of data to identify patterns and relationships that identify and predict the success of a candidate in a role.

It creates better outcomes by recommending the best fit using data-driven hiring strategies.

It’s a relatively new concept, but it has seen more accurate and reliable predictions of success than traditional hiring methods, such as assessments and interviews. That’s because it uses more than just a gut feeling. Instead, it leverages historical data and machine learning techniques as well as statistical algorithms to identify candidates.

The Data Needed to Enable a Predictive Hiring Process

Machine learning technology needs data points to be able to make predictive hiring as accurate as possible. Common information needed includes:

1. Information about the candidate, such as their education, work experience, certifications, and skills.
2. Information about the position for which the company is hiring. This includes the job title, job description, and required skills.
3. Information that details the recruitment channels used to find candidates, like social media and job portals.
4. Assessments or evaluations of the candidate that are done during the hiring process, such as personality tests and interviews.
5. Historical hiring data on past candidates who are now employees. You can use their qualities to train the predictive hiring model.

Once the aforementioned data points have been logged, the data analysis can begin. When the data has been analyzed, patterns and characteristics among the points can be identified and used to make decisions.

How Can Machine Learning Assist with Predictive Hiring?

Machine learning technology is used in many ways across the hiring process to make it more efficient.

1. Screening resumes

Resume screening is a challenge for recruiters, who often waste a lot of time and resources sifting through piles of resumes that don’t even meet the basic job requirements.

When done manually, the task is tedious, but it is one that predictive hiring can make more efficient. Machine learning technology can identify resumes that have specific qualities, such as relevant experience and skills. You can also train the algorithm to search for specific keywords. It can then only forward these resumes to the next round in the process.

The result for the recruiter is that the only resumes that they have to go through are the relevant ones.

2. Matching candidates

After screening all the resumes, machine learning technology can match candidates to job openings based on their skills, experience, and education. This is especially useful if more than one position is open at your company. This kind of predictive hiring matches the best candidate for the role.

3. Predicting performance

By analyzing various data points such as job history and personality traits, machine learning algorithms can predict the performance of a specific candidate in a role. The algorithms can also analyze the performance data of previous employees who were in similar roles to predict the performance of new candidates.

Beyond The Hire

Even after the hiring process, machine learning technology can help to optimize efficiency. For instance, if you struggle with scheduling employees using traditional paper models, then there is software that can help you to optimize your schedules so that your teams stay on track. This also helps you to plan things, such as interviews and assessments for potential candidates.

Benefits of Using Predictive Hiring 

There are several benefits of using machine learning for predictive hiring.

First, machine learning analyzes data objectively, providing unbiased recommendations. This allows for greater diversity and inclusivity, as applicants have equal chances at success. Candidates are judged according to their skills and experience rather than demographic factors.

Second, predictive hiring improves recruitment accuracy and efficiency. The technology can also quickly write job descriptions, and schedule assessments as well as interviews.

Third, machine learning technology can lead to improved employee retention. It’s designed to find the best candidates, which reduces turnover and improves both employee and employer satisfaction.

Obstacles Facing Predictive Hiring

As with any technology that is still somewhat in its infancy, developing the perfect predictive hiring software does not come without its fair share of obstacles.

For instance, to train the algorithms effectively, there needs to be a lot of high-quality data so that the predictions are accurate. This is a challenge for businesses that haven’t gathered enough data or make poor data entries.

The lack of human touch is another obstacle. While machine learning algorithms may make the hiring process more efficient, they cannot replace the judgment and intuition of experienced recruiters who may recognize transferable skills.

There are also some ethical concerns surrounding machine learning in predictive hiring. If not trained properly, the technology can perpetuate bias rather than diminish it.

Is Predictive Hiring the Future? 

With AI advancing at such a rapid pace and machine learning streamlining and automating so many processes in the workplace, it’s safe to say that predictive hiring will play a major role in recruitment in the future. It’s one of the major tech trends in hiring and it’s well worth keeping this in mind when applying for a job in today’s marketplace.

Empire Resume Will Help You Get Hired

We have greater than a 97% success rate landing our clients’ interviews!

CLICK HERE TO LEARN MORE AND GET STARTED

Maria Gold is a Content Manager/Writer for Empire Resume. She is dedicated to helping educate and motivate people with the latest career articles and job search advice. Her interests range from writing to programming and design. She is also passionate about innovation, entrepreneurship, and technology. 

Related Articles: