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To tailor your resume to a job description, start with your job titles

Add the posting's focus to a job title where your work shows it, then make your summary and skills tell the same story.

8 min read

You have your resume and a job description open, and the advice is to tailor one to the other. Most people start with the bullets.

Tailor your resume in the order a recruiter reads it, job titles first: as we see it, if the titles don't show the role, nothing below them gets read.

If you came to match your resume to a job description and get a resume match score, this post gives a method you can do by hand, and no score.

We think a recruiter reads in layers, and only the first screen has been measured

As we see it, a recruiter reads a resume in layers, and each layer decides whether the next one is read. The first scan takes in your job titles, employers and years, to see whether your career looks like this role. If it does, they read your summary to see whether it adds to that story, then check your hard skills, and only then skim the bullets.

You can't change your employers or your years. Of the first layer, only the titles are yours to change. So tailor in the order a recruiter reads, titles first.

Tailor in the order a recruiter reads, titles firstHow TeaKo sees it, drawn as four dashed boxes from top to bottom. Layer 1: titles, employers and years, which asks whether this career looks like the role; of these you can change only the titles. If it fits, the read goes on. Layer 2: the summary, does it add to the story. Layer 3: hard skills, do they back the story. Layer 4: bullets, skimmed for proof. This is our view, not a measured reading order.How we see itTailor in the order a recruiterreads, titles firstThe four layers of a read, and what each checks1Titles, employers and yearsDoes this career look like the role?You can change only the titles.If it fits, the read goes on2SummaryDoes it add to the story?3Hard skillsDo they back the story?4BulletsSkimmed for proofHow TeaKo sees it.TeaKo
How TeaKo sees a recruiter's read: four layers, each deciding whether the next is read. This is our view, drawn in dashed lines; no study has measured this order after the first scan.

What studies have measured is the first screen, and there recruiters weigh experience far ahead of education. In a SHRM study of more than 1,000 recruiters, published in 2023, 84 in 100 rated years of experience important at the first resume screen and 35 in 100 rated education level important.

Recruiters weigh experience far ahead of educationTwo horizontal bars. Of the recruiters surveyed, 84 in 100 rated years of experience important at the first résumé screen, and 35 in 100 rated education level important. Source: SHRM survey of more than 1,000 recruiters, 2023; the country is not stated in our source.Recruiters weigh experience farahead of educationRecruiters who rated each important atthe first résumé screen, in 100Years of experience84 in 100Education level35Source: SHRM survey of more than 1,000 recruiters,2023. Country not stated in our source.TeaKo
Share of recruiters who rated each factor important at the first résumé screen. SHRM survey of more than 1,000 recruiters, published in 2023; the country is not stated in our source.

That is what recruiters say they weigh. It says nothing about what is read after the first scan.

In a 2023 eye-tracking study of 221 US recruiters who hire computer-science graduates, the time spent on the Experience section was one of the main signals of whether a resume was passed on to the next step.

Start with your titles and add the posting's focus where your work shows it

Before you touch the resume, write the story in one line. The story is the claim that you are the person this posting describes, and you take it from the posting's title and daily duties.

Take a data scientist with eight years in retail and fintech who applies for a Growth Data Scientist role. The story is "a data scientist who runs growth experiments".

Both jobs on the resume carry the title "Data Scientist". In the retail job, three of the five bullets are growth work: two sets of A/B tests and a model of which customers respond to offers. In the fintech job, most of the work is risk.

So the retail title gains its focus and becomes "Data Scientist, Growth", and the work behind it moves up: the three growth bullets go to the top of the role. The fintech title stays, and its churn model for app users moves to the top.

The title gains its focus, and the work behind it moves upAn example résumé, made up for this article: the experience section of a data scientist applying for a Growth Data Scientist role, before and after. Before, the retail role, 2021 to now, is titled Data Scientist, with bullets in this order: demand forecasts for stores, data pipelines, A/B tests on checkout, A/B tests on email offers, a model of which customers respond to offers. The fintech role, 2018 to 2021, is titled Data Scientist: credit risk model, fraud model, churn model for app users. After, the retail title becomes Data Scientist, Growth, and the three growth bullets move to the top: A/B tests on checkout, A/B tests on email offers, the model of which customers respond to offers, then demand forecasts and data pipelines. The fintech title stays Data Scientist because most of that work is risk, and the churn model moves to the top. Nothing was added.The title gains its focus, andthe work behind it moves upExample: a data scientist applies fora Growth Data Scientist roleBeforeData ScientistRetail company, 2021 to now•Demand forecasts for stores•Data pipelines•A/B tests on checkout•A/B tests on email offers•A model of which customersrespond to offersData ScientistFintech company, 2018 to 2021•Credit risk model•Fraud model•Churn model for app usersAfterData Scientist, GrowthRetail company, 2021 to now•A/B tests on checkout•A/B tests on email offers•A model of which customersrespond to offers•Demand forecasts for stores•Data pipelinesData ScientistFintech company, 2018 to 2021Title stays: most of this work is risk•Churn model for app users•Credit risk model•Fraud modelShaded: a new focus, or work moved up.Nothing was added.Example résumé, made up for this article.TeaKo
Example résumé, made up for this article: the experience section before and after. The retail title gains the focus its work shows; the fintech title does not, because most of that work is risk. Shaded lines are a new focus or work moved up. Nothing was added.

A title with a focus is your real title plus the focus the work under it shows, at the same level. The bullets under a title are its proof when the recruiter does skim, so a title and its top bullets change together.

The focus matters because "Data Scientist" alone says little about which kind of work you did. A 2018 study of US professional job postings found that the same occupation asks for different skills at different firms, and that requirements vary a lot even within narrowly defined jobs.

Postings for a single role differ in our own count too. On 12 November 2026, on the employer career sites TeaKo reads in the US and Canada, of the more than 830 data scientist postings that list skills, 78 in 100 named Python, 63 in 100 named SQL and 52 in 100 named machine learning. That count says nothing about growth roles in particular.

No study has tested a title with a focus, so this step is our view. The nearest evidence is about dates. A preregistered field experiment with real UK employers, published in 2023, found that listing past jobs by years worked rather than by start and end dates raised callbacks by roughly 8 to 15 percent in relative terms, most for people with employment gaps.

The experience on those resumes stayed the same and only its display changed. It is no reason to change how you list your own dates.

Then rewrite your summary so it tells the story your titles now tell

In our account the summary is the second layer, read to see whether it adds to the story the titles started. You can only summarise that story once the experience below tells it.

Write two or three lines that say what your titles and top bullets now show. In the example, the old summary led with machine learning models, forecasts and data pipelines. The new one reads: "Data scientist who runs A/B tests and models customer behaviour, from checkout and email offers to app churn, in retail and fintech."

Every part of it is already on the page: the A/B tests ran on checkout and email offers, and the models are the offer response model and the churn model.

We have found no study that measures how a summary or a skills line is read, so this step and the next are our reasoning.

Last, keep the hard skills that back the story and cut the ones that point to another role

The skills line is the third layer. It is checked against the claim your titles and summary made, so it can confirm the story and cannot make it for you.

Pick the hard skills that back the story and list them first, in the posting's words. "Experimentation" in a posting and "A/B testing" on a resume can be the same work, and the posting's word is the one a reader will look for.

Then cut the skills that point to another role. In the example, nine skills become five: A/B testing moves from last place to first, and TensorFlow, Spark, Airflow and time series go. The summary and skills now tell the same story as the titles.

Summary and skills now tell the same story as the titlesThe same example résumé, made up for this article, before and after. Summary before: Data scientist with eight years building machine learning models, forecasts and data pipelines in retail and fintech. Summary after: Data scientist who runs A/B tests and models customer behaviour, from checkout and email offers to app churn, in retail and fintech. Hard skills before, nine of them: Python, R, SQL, TensorFlow, Spark, Airflow, time series, Tableau, A/B testing. After, five kept, in this order: A/B testing, SQL, Python, R, Tableau. Cut because they point to another role: TensorFlow, Spark, Airflow, time series.Summary and skills now tell thesame story as the titlesExample: the same résumé, before and afterSummaryBeforeData scientist with eight years buildingmachine learning models, forecasts anddata pipelines in retail and fintech.AfterData scientist who runs A/B tests andmodels customer behaviour, from checkoutand email offers to app churn, in retailand fintech.Hard skillsBeforePythonRSQLTensorFlowSparkAirflowtime seriesTableauA/B testingAfter: kept, in this orderA/B testingSQLPythonRTableauCut: they point to another roleTensorFlowSparkAirflowtime seriesExample résumé, made up for this article.TeaKo
Example résumé, made up for this article: the summary and hard skills before and after. The summary now says what the titles and top bullets show; the skills line keeps the five that back it and cuts four that point to another role. Nothing was added.

Tailoring changes the order, emphasis and wording of what is on your resume, never the facts.

Layout, length and action verbs are general resume writing, which how to write a strong resume covers.

The whole method is six steps, taken from the top of the page down

  1. Write the story in one line, from the posting's title and daily duties.
  2. For each past role, add that focus to the title only where the work under it shows it. Keep your level and the facts.
  3. Under each role, move the work that proves the story to the top.
  4. Rewrite your summary in two or three lines so it says that story.
  5. Pick the hard skills that back it, list them first in the posting's words, and cut the ones that point to another role.
  6. Compare your resume to the job description top-down, as a recruiter would read it: titles, summary, skills, first bullets. Or give the posting to TeaKo's resume tailoring, as a link from the employer's own careers page or a pasted description (job-board links are turned away): it suggests titles from your own bullets and rewrites the summary, skills and bullets, checking every line against your resume.

The resume you send holds the same facts as the one you started with. What changed is where a reader meets them: the role in your titles, the story in your summary, and the proof at the top of each job.

Paste a job link from the employer's own careers page and add your resume. TeaKo suggests a title for each role from what its bullets support, rewrites the summary, hard skills and bullets for that job, and checks every rewritten line against your resume.

Tailor your resume to a posting