If you’re here, you’re on the right track. That said, most job seekers do not apply with resumes optimized for the way recruiters use ATS and don’t get the consideration they expect as a result. The common claim that “75% of applicants are rejected by ATS” is simply not true. The idea that ATS “robots” are auto-rejecting thousands of applicants without human input is a myth. In reality, these algorithms are unreliable and most recruiters still manually review as many resumes as they can. In theory, tools like this would save recruiters time and help them focus on top candidates. For example, Taleo, one of the most-used ATS in the United States, has a feature called ReqRank that automatically compares applicants’ resumes to the job description and ranks them based on match rate. It’s true that ATS have search, filter, and ranking features that recruiters can use. This includes sticking to formatting guidelines that ensure your resume will display as intended within a digital profile and targeting your resume keywords based on what the recruiter is looking for. Through resume optimization, you can significantly increase your visibility and interview chances by applying with a resume that is ATS-friendly, or written to be compatible with the way real recruiters use these systems. There are real recruiters using the ATS software who make the decision to reject or approve an applicant like you. You can click the Example and then click the Compare button. Just paste the original and the changed text in respective boxes and click the Compare button. You don’t need shady tricks to “beat” an ATS because an ATS won’t be rejecting your resume. Find the difference between 2 text files. For each document or text piece, we identify a subject, main topics, key words, and phrases that form its semantic core, and then match the identical and synonymously related units. ![]() ![]() How do I beat Applicant Tracking Systems (ATS)? We employed natural language processing algorithms and machine learning to match and combine pieces of textual content based on a degree of their semantic similarity.
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