It’s 2026 and competitive jobs can get thousands of applicants — underscoring the importance of relevant, well-tailored resumes. That does not mean blind keyword matching. Here are some important things to keep in mind.

1. Don’t blindly put your resume in ChatGPT

I know, I know — the temptation has never been stronger. They’re probably not going to respond anyway, right? What’s the point of spending 30 minutes researching the company, strategizing, writing, and editing?

The problem is that 70–80% of recruiters are actively on the lookout for AI-generated language and will dump your resume if they think it is. With so many people using the same tools, recruiters are reading AI slop every day so they can discern it pretty quickly. (We dig into the math behind why getting a job is so hard in 2026.)

It’s important to make sure your resume doesn’t sound like it’s written by AI. The bar isn’t “did AI touch this?” — it’s “does this read like a real person wrote it?”

2. Tailor to content, not keywords

Contrary to popular belief, rewriting your resume with exact keywords is not going to help you surpass ATS. Especially with large language models and modern AI, semantic matching rather than keyword matching is what dominates and will also help you with the human review afterward. (If you’re wondering whether tailoring is even necessary, we break that down in Do I need to tailor my resume?)

And when there is keyword matching — it’s often something that is not directly mentioned in the job posting but what someone with that experience would actually say.

What the posting says
“Experience with geographical analysis”
What they’re actually looking for
“GIS”
Because no one in the industry actually calls it “geographical analysis.”

3. Focus on what you did, not how you did it

As a professional data scientist, I would see this all the time in internship or early-career resumes. “Used Python to do X.” You want to demonstrate that you know a certain language, are familiar with a particular library or tool. But this is probably the biggest giveaway that someone lacks experience. It’s sort of like saying “I used my notebook to do my lab.”

Instead, you should focus on the impact and content of what you did. The languages, tools, and technical details are secondary and that’s what you’ll discuss in the interview.

Before
“Used Python and Pandas to analyze user engagement data”
After
“Increased web traffic by 20% by designing and deploying A/B tests across 3 product surfaces”