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How Machine Learning Helps Job Seekers

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Searching for a job can sometimes be a daunting task. Not only does the decision have significant consequences on your overall mental and physical health, financial well-being, and general happiness, but it can have long-term implications in terms of your career path. The stakes are quite high, indeed. Add on top of that the sheer number of options – many of which might be with companies you’ve never heard of before – and it’s easy to understand why more than a few job seekers might be getting overwhelmed by the whole process.

Fortunately, with the many technological advancements that have been made in recent years, today’s job seekers can benefit from sophisticated tools to help them in their job search.

In this short article, we’ll be taking a closer look at one such tool: machine learning. We’ll begin by taking a look at what exactly machine learning is, how it is being used by recruiters and certain job portals, and how job seekers can use this new technology to their advantage.

What Is Machine Learning?

In simplistic terms, machine learning consists of the development and application of computer programs that are able to – with limited direction or instruction – process, analyze, and interpret a considerable amount of data. Through a series of algorithms, the computer program will then take the results, recognize patterns, and adapt its programming accordingly.

Machine learning is a type of artificial intelligence that enables the computer to become more accurate at detecting patterns and making predictions without the need for updating its programming in the function of the data it receives – the computer is able to do that itself.   

What Is the Difference Between Machine Learning and Artificial Intelligence?

While in many circles, and depending on the context, we could use the two terms interchangeably, there does exist a distinction.

Artificial intelligence (AI) describes a computer program’s ability to emulate human thought and carry out tasks based on its emulated human thought. On the other hand, machine learning (ML) refers to algorithms that give the computer program its ability to identify patterns and make decisions accordingly.

In a nutshell, machine intelligence is the algorithm, process, or programming that allows a computer to emulate human thought. Machine intelligence answers the question “how”,  whereas artificial intelligence answers the question “what.”

What Are Some Common Examples of Machine Learning?

We may not be aware of it, but we benefit from machine learning every day in a number of ways. Some common examples of the application of machine learning include:

  • Recommendations – Whether they’re coming from an online shop, a social media platform, or a streaming provider, the recommendations we are shown come as a result of the computer programming interpreting the data we give it and making a prediction: machine learning.
  • Chatbots – Now a ubiquitous tool for customer service – or any context where a company, brand, or even a website wishes to engage with a user or customer – chatbots rely on the concepts of machine learning to interpret data and make accurate predictions.
  • Computer-assisted translations – Powerful translation programs work based on the computer program’s ability to analyze and interpret data collected from the net (how words and phrases are being translated by other sources) and then make their own translation choices accordingly.
  • Business Process Management (BPM) – Ultimately, the business process will be defined and adjusted according to consumer behavior. Machine learning is ideal for analyzing and interpreting insights in customer behavior as well as for predicting future customer behavior trends. Machine learning is an ideal tool for carrying out continuous improvement steps for process optimization.

Machine Learning Used on Popular Job Portals

In today’s highly competitive job market, online job portals are continuously striving to provide job seekers with access to the most accurate information (based on each individual job seeker’s own unique set of criteria). This is why we are seeing more and more online job portals – like Lensa Inc. – incorporating machine learning in their platforms.

A powerful computer program based on the concepts of machine learning will analyze and interpret data from millions of job offers (both current and historical). From these data points, the computer program will then be able to detect patterns such as:

  • What type of soft skills are most in demand currently? Which soft skills are trending downward (diminishing as a priority among recruiters? And which are experiencing an upward trend?
  • What type of technical skills are most in demand currently? Which technical skills are trending downward (diminishing as a priority among recruiters? And which are experiencing an upward trend?
  • What are the likely steps that a given job offers that could lead to advancement along a given career path?
  • What kind of credentials does a given sector of activity require? Which credentials are trending downward (diminishing as a priority among recruiters? And which are experiencing an upward trend?
  • Which job offers do not require prior work experience in the field?
  • What kind of prior work experience best translates across different sectors of activity?

Advantages of Job Portals That Use Machine Learning

When we get down to the nuts and bolts of the job search, what job seekers are looking for are options. However, it’s not simply a numbers game. These options need to be relevant to the job seeker’s priorities and relevant to the strength and/or weakness of their candidacy. It does the job seeker no good if they are given a hundred options yet they are overqualified or not at all qualified for any of them. On a similar front, what good does it do to job seekers if the options they are presented with simply do not meet their stated criteria?

Filtering

First and foremost, machine learning allows the job portal to filter out all the offers that are irrelevant to the given job seeker – either because the offer doesn’t meet their defined set of criteria or because the job seeker him or herself is simply not qualified (or overqualified).

Searching for a job is a time-consuming process. Filtering out all the irrelevant options goes a long way toward saving the job seeker precious time. Furthermore, searching for a job can often be a very stressful and demoralizing experience. How much more stressful and demoralizing would it be if the job seeker were constantly applying for jobs they stand little to no chance of landing? Or if the job seeker were spending a lot of time applying for jobs they wouldn’t end up taking even if it were offered to them?

Insights

Often, a job seeker doesn’t have a clear idea of what they want. Perhaps, they are relatively new to the workforce or they are looking to change career paths completely. A job portal that uses machine learning will be in a better position to offer the undecided job seeker insights into what kind of jobs or what sectors of activity they may find the most rewarding.

Insights into what jobs are in demand, what jobs are fading in demand, and what jobs are likely to trend in the future are invaluable to a job seeker, especially to job seekers who don’t have a specific idea of what kind of work they are best suited for or that they would like to do.

Projecting a Long-Term Career Path

In the past, online job portals were primarily tailored to offer job seekers options on the next job they could take. However, thanks to advancements in technology – notably artificial intelligence and, more precisely, machine learning – online job portals are now able to make predictions and offer insights into a job seeker’s overall career trajectory.

Based on a job seeker’s profile – the soft skills and technical skills they possess, their personal characteristics, achievements, goals, etc., – an online job portal that uses machine learning has collected, analyzed, and interpreted enough data that stretches out across the entire careers of millions of workers. From this data, the job portal is able to suggest alternative career paths the job seeker might otherwise have never considered – or they simply didn’t know such a career path existed.

How Machine Learning Has Impacted Recruitment

It isn’t only the job seeker that has benefited greatly from recent advancements in artificial intelligence. Today, recruiters from across all sectors of activity – and regardless of the size of the organization they are recruiting for – are taking advantage of machine learning to help them in the recruitment process.

Screening Candidates

Based on data collected that relates to successful and unsuccessful employees, machine learning can then filter out candidates that are unlikely to be a good fit for the company. This is something, in the past, recruiters would have to do themselves. However, the disadvantage of having humans do this task is that, firstly, it takes a considerable amount of time. And secondly, humans are susceptible to biases.

A bias means that recruiters – whether consciously or unconsciously – are susceptible to making decisions that are not based on any practical information but rather are based on emotional or cognitive prejudice (both in favor of a candidate or to the detriment of a candidate). A computer algorithm is not susceptible to biases. This means the decisions they make or suggest are based on hard, cold facts, statistics, and probability.

Testing Candidates

Similar to the initial screening process, when it comes to testing or verifying if a given candidate has the skills or know-how needed to be considered for the job, this process is also incredibly time-consuming and susceptible to human biases.

With machine learning, candidates can be tested through chatbots and artificial intelligence skill assessment tools.

Onboarding

The recruitment process doesn’t necessarily stop at the signature of the contract. The new hire needs to undergo the process that will transform them from recruit to productive and integrated employee. This process is known as onboarding.

Machine learning provides companies with data and insights so that they can make employee onboarding improvements.

How Job Seekers Can Benefit From Machine Learning

Beyond getting shown job offers that are more likely to meet their stated priorities and that are more likely to be a better fit, given their skill set and prior work experience, job seekers can use machine learning for much more.

Crafting a Compelling Resume

The job seeker’s resume needs to be concise. It needs to grab the recruiter’s attention and convince them that their candidacy should be given serious consideration – all while competing with hundreds of similar resumes being submitted at the same time.

Machine learning can help the job seeker by showing them:

  • What soft skills (also known as transferable skills) are recruiters looking for?
  • What technical skills are likely to lead to employment?
  • What personal characteristics are in high demand currently?

With these insights, the job seeker can then craft their resume to highlight what it is recruiters are looking for, thus, increasing the chances that their resume will be seen as appealing by the recruiter.

Gaining Insights Into the Longer Career Trajectory

Most jobs are considered stepping stones that can lead to bigger and better jobs down the road. However, in reality, this is not always the case. Many people bounce around from job to job and they are not advancing. In contrast, they are stuck in a pattern of lateral movements.

The machine learning that is being implemented and used by today’s best job portals can help to rectify or avoid this rut of lateral movement. The fact that the artificial intelligence program can interpret data from millions of job seekers and millions of job offers means that this program is able to detect patterns and make predictions with a high degree of accuracy.

  • Which sectors of activity are likely to promote from within?
  • What prior work experience does a given position typically call for?
  • Which jobs can then be used as stepping stones to land a better job in that sector?
  • Which jobs provide work experience that is not particularly sought after by recruiters, that doesn’t necessarily translate well to other jobs?

In a Nutshell

Machine learning is the development of algorithms and computer programs that allow computers to analyze large sets of data, make decisions, and adapt accordingly. Machine learning is being used for a wide range of functions, including recruitment and job search. Additionally, Ai learning programs have become the pathway to the future with enterprises such as Levi Strauss implementing it in their mandatory onboarding program.

Thanks in part to machine learning, today’s job seekers are less likely to have to deal with irrelevant job offers. They stand a higher chance of finding job offers they are qualified for and that they want. Furthermore, they can benefit from machine learning to gain insights into the steps along various career paths and how to better prepare for the eventual curves coming their way.

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