How Applicant Tracking Systems Use Pattern Recognition to Select Candidates for Job Interviews

By Jeff Altman, The Big Game Hunter
In this video, I dive into the world of Applicant Tracking Systems (ATS) and explore how they use pattern recognition algorithms to identify the most qualified candidates for job interviews. You’ll learn how these systems work, what they look for, and how to optimize your resume to increase your chances of getting selected. Whether you’re a job seeker or a recruiter, this video will provide valuable insights into the increasingly automated hiring process.

00:00 Intro
01:27 What is pattern recognition?
02:00 Keyword matching
03:09 Candidate ranking
04:17 Job posting analysis
05:28 Interview selection
06:16 Resume parsing
07:00 Summary
07:30 Outro

ATS Myths

Applicant tracking systems are software that is used for a couple of different things. But the basic thing is about managing the recruitment process by organizing job postings, matching resumes and applications, and tracking the hiring process. One of their key features is the ability to use pattern recognition to select people to interview.

I’m Jeff Altman, The Big Game Hunter. I’m called The Big Game Hunter, it started when I still did recruiting. I’d hunt down leaders and staff for organizationscropped-whats-your-problem-1.jpg and did for a long time very successfully. And now I provide no BS coaching and career advice to people globally related to job search, hiring more effectively, managing and leading better, and a whole host of different things because I make things easier for people so that you don’t have to learn through trial and error.

Now, if you’re job hunting, no matter whether it’s allegedly good times or bad times, competing for certain jobs is very competitive. Companies on their side are looking for efficient ways to filter all the resumes that they’re receiving. And this is where pattern recognition is used by the systems to narrow the pool for them to interview.

What pattern recognition is, is the ability of a system to identify patterns in data and use those patterns to make predictions about, in this case, people and who they should interview. As a result, it’s used, the algorithms are used to analyze resumes and job applications to identify the most qualified people for a given opening. I’m not saying it’s right, but this is what it’s supposed to do. And this involves a couple of different steps.

And the first one is of course, keyword matching, which is the most basic form of pattern recognition used by applicant tracking system software. It’ll scan a resume for specific keywords related to a job posting, such as titles, skills, specific knowledge, specific software knowledge. If a candidate’s resume contains those keywords, the system is going to rank the candidate higher in the list of potential choices for the recruiter. As a result, let me give you an example. If a job posting wants someone with experience in Java programming, the applicant tracking system can scan resumes for the keyword Java, and identify resumes that mentioned Java programming, in your experience or in the skills section. It’s very limited. It can certainly lead to false positives. For example, somebody mentions Java programming, but it’s escalated to a manager role or higher. They did it in the past, but certainly they’re not going to do it today.

But back to candidate ranking, it’s using pattern recognition to select people to interview, ranking them based upon what the system believes their qualifications are. It has a scoring system that takes into account the requirements of the job, a person’s education and experience and other perceived relevant factors. The scoring system is designed to identify people who are the best match for a job based upon qualifications. For example, if they want someone with a bachelor’s degree and five years of relevant work experience, strong communication skills, it may score higher for someone who only has a high school diploma and limited work experience. So just to be clear about that, when they are evaluating people, there are primary factors and then there are secondary ones where they project whether or not someone’s communication skills, for example, or degree are going to allow this person to rank higher or in this case lower.

It also will do a job posting analysis. And thus, in analyzing the posting, it will use natural language processing algorithms to identify skills, qualifications and experience required for a job. This algorithm will look for keywords or phrases that are commonly used with the job. And as a result, based upon where it appears in your resume, is going to make decisions about whether or not the experience is recent or older. And thus, once the posting has been analyzed, it’sblockchain developer going to compare skills and qualifications and rank people with the result being you know, it’s taking a shot

Reverse Engineering the ATS for Results

It also does interview selection. And again, this step in pattern recognition selects the people to interview who are more likely to be successful throughout the process. And it has predictive analytics algorithms to identify primary and secondary factors, leading to a successful interview. And some of those may involve who the employer is, what your academics are, like, how long you’ve worked at a particular firm, as such, think in terms of work history, and comparing the patterns of existing people who’ve been successful in the organization with those on the outside and making choices like based upon that.

Lastly, I’m just gonna point that out, it does resume parsing, and that’s no big deal for you, per se, if anything, it’s expected. On the company side, they think of it from the standpoint of, in the future, if they want to get in touch with you, they want to be able to search their system to locate you. And they don’t . . . unstructured data is going to make it much more complicated to find someone because they need to search by zip code, they need to search by skills, by degrees, but what you’ve done, and having it parsed in particular ways not only helps them with government reporting but helping to identify people in the future who might be qualified for.

So I’ll just simply say, pattern recognition is a key feature in any applicant tracking system. By using natural language processing, scoring algorithms, and predictive analytics, an applicant tracking system will quickly, I can’t say efficiently but more efficiently than not, identify people to interview for any given position. And thus, save a firm time and money in the recruitment process.

I hope you found this helpful. I’m Jeff Altman. Visit my website, There’s a ton in the blog that can help you. Plus, if you want to schedule a time for a free discovery call or coaching session with me, you can do it at the site. Or if you just have a couple of questions, which you can do to schedule time for Trusted Advisor Services where you can ask questions of me in a 30 minute period.

And at the site, you can find out about my video courses, books and guides. There’s just a lot there that’s going to help. Also connect with me on LinkedIn at Mentioned that just saw the video. I like knowing I’m helping some folks. And once we’re connected, my network is going to be a lot larger than yours probably, and thus you’re going to be expanding it a lot. Have a terrific day and most importantly, be great!

Making Your Resume ATS Likeable


Jeff Altman, The Big Game Hunter | Job CoachJeff Altman, The Big Game Hunter is a coach who worked as a recruiter for what seems like one hundred years. He is hired to provide No BS Career Advice globally. That can involve job search, hiring staff, management, leadership, career transition and advice about resolving workplace issues. Schedule a discovery call at my website,

He is the host of “No BS Job Search Advice Radio,” the #1 podcast in iTunes for job search with over 2500 episodes.

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