Most rejections happen before a human reads your CV. An applicant tracking system scans the file first, and if it cannot find the words it was told to look for, your application sinks to the bottom of the pile. The fix is not gaming the software. It is understanding what it wants and giving it exactly that, in language that still reads well to the recruiter who opens the file next.

What ATS keywords actually are

Keywords are the specific terms a recruiter or hiring manager decides matter for a role. Some are hard requirements like "Python", "SOC 2", or "accounts payable". Others describe the level or context, such as "team lead" or "B2B SaaS". The system does not understand meaning the way a person does. It matches strings. If the posting asks for "project management" and your CV only says "ran projects", the match may not register. That gap, repeated across a dozen terms, is what quietly filters you out.

Extracting keywords from the job description

The job description is the answer key. Read it twice. On the first pass, highlight every noun and noun phrase that names a skill, tool, certification, or responsibility. On the second pass, notice which terms repeat or sit near the top, because those carry the most weight.

• Pull the "requirements" and "responsibilities" sections apart line by line
• Note the exact spelling and casing the company uses
• Watch for terms mentioned two or three times, since repetition signals priority
• Separate must-haves from nice-to-haves so you know where to focus

If you have several similar postings, compare them. The words that show up again and again across the industry are your safest bets.

Hard skills, soft skills, and exact-match phrasing

Hard skills are concrete and easy to match: software, languages, methods, tools. Put these in verbatim. If the posting says "PostgreSQL", do not write "Postgres". Match the string.

Soft skills like communication or leadership matter to the human reader but rarely drive the ATS score, so do not lean on them to carry the file. Where a phrase has a common variant, include both once if it fits naturally. Writing "customer relationship management (CRM)" covers the acronym and the spelled-out version in one line.

Tools and methods to find keywords

You do not need paid software, though it helps.

• Paste the job description into a free word-frequency counter to surface the most repeated terms
• Compare three to five postings for the same title and log the overlap in a simple list
• Check LinkedIn profiles of people already in the role to see the vocabulary they use
• Read the company careers page for recurring language about how they describe the work

Keep a running keyword sheet per target role so you are not starting from zero each time.

Placing keywords naturally

Placement matters as much as presence. Spread terms across the summary, the skills section, and the bullet points under each job, because context makes them credible. A skill listed once and then demonstrated in a bullet reads as real experience, not a checklist.

• Work the top five or six critical terms into your summary and recent roles
• Attach each hard skill to a result, for example "cut reporting time using SQL and Power BI"
• Use a dedicated skills section for tools that do not fit into a sentence

Avoid stuffing. Pasting a wall of keywords in white text or a hidden block gets flagged and, worse, insults the recruiter who eventually reads it.

Tailoring per application

One master CV cannot win every role. Keep a full version with everything you have done, then cut it down and re-weight it for each application. Swap in the exact terms from that specific posting, reorder your bullets so the most relevant work sits first, and drop anything irrelevant to this job. Fifteen focused minutes per application beats sending the same generic file forty times.

Common mistakes

• Copying the whole job description into your CV, which reads as obvious padding
• Using acronyms only, or spelled-out terms only, instead of both
• Stuffing keywords with no supporting evidence
• Ignoring casing and exact spelling the employer used
• Relying on graphics or tables that many parsers cannot read

Takeaway

Treat the job description as a brief, mirror its language honestly, and back every keyword with a real result. Do that per application and you clear the filter without ever sounding like you wrote for a machine.