Full example

Same candidate, two resumes

Here is what Carriv does, in full and with no sign-up: a posting, a generic resume, and the resume tailored to that specific posting. Nothing is invented in between — the content comes from the same profile, selected, reordered and reworded.

Demonstration. The candidate, her employers and the hiring company are fictional. The two scores are the ones Carriv’s engine computes on these two texts against this posting.

The posting

Senior Data Analyst

Hélio Santé (fictional company)

We are looking for an analyst to structure our clinical data and equip care teams. You will design the data models, build the ETL pipelines, and ship Power BI dashboards used daily on site. Strong SQL required. An interest in data governance and healthcare-sector experience are assets.

43 %

ATS match

Generic resume

The same one for every application

Summary

Versatile and rigorous analyst with excellent teamwork skills and a strong ability to adapt. Passionate about numbers and problem-solving, I am looking for a stimulating role where I can put my skills to use.

Experience

Data Analyst — Groupe Vireo (2021 – present)

  • Responsible for producing monthly reports for management.
  • Extracted and cleaned data from various databases.
  • Took part in various cross-functional projects within the team.
  • Wrote technical documentation and supported users.

Skills

Excel · SQL · Python · Communication · Teamwork · Rigour · Autonomy · Time management

88 %

ATS match

Resume tailored to this posting

Generated in one pass, from the same profile

Summary

Data analyst with 6 years in healthcare, specialised in clinical data modelling and building ETL pipelines. Designs and maintains Power BI dashboards used daily by care teams, with a constant focus on data quality and governance.

Experience

Data Analyst — Groupe Vireo (2021 – present)

  • Designed 12 Power BI dashboards consulted weekly by 40 clinical stakeholders, replacing a manual monthly reporting cycle.
  • Built the ETL pipelines consolidating 7 clinical data sources into a single warehouse, in SQL and Python, run through daily automation.
  • Reworked the data modelling of the patient domain and defined the data-quality rules applied at ingestion, cutting reconciliation discrepancies by 60%.
  • Formalised the warehouse data catalogue and governance rules, adopted by all three analytics teams in the organisation.

Skills

SQL · Power BI · Data modelling · ETL · Data pipelines · Data quality · Data governance · Python · Clinical data

What changed, term by term

The posting’s keywords

The score is not the model’s opinion: Carriv computes it in code, looking for the exact form of each posting keyword in the resume text. Here are the 14 terms extracted from this posting.

TermWeightBeforeAfter
SQLRequired Covered Covered
Power BIRequired Missing Covered
data modellingRequired Missing Covered
ETLRequired Missing Covered
dashboardsRequired Covered Covered
data pipelinesRequired Missing Covered
clinical dataRequired Missing Covered
healthcareRequired Covered Covered
data governanceAsset Missing Covered
PythonAsset Covered Covered
dbtAsset Missing Missing
data qualityAsset Missing Covered
stakeholdersAsset Covered Covered
automationAsset Covered Covered

The term left missing

Why “dbt” was not added

Nothing in the candidate’s profile mentions dbt. So Carriv does not add it, even though it would have raised the score: the tool only surfaces what you have actually done. The ATS report flags it as a genuine gap, with a suggestion for closing it — train for it, or drop it from your target.

And the letter

Written in the same motion

The letter does not repeat the resume: it takes two real facts from the profile and ties them explicitly to the posting’s requirements. Excerpt.

Your posting mentions structuring clinical data and equipping care teams: that is exactly the work I have been doing for three years at Groupe Vireo. There I built the ETL pipelines consolidating seven clinical sources into a single warehouse, then the twelve Power BI dashboards that around forty stakeholders consult weekly — clinicians, not analysts, which changes everything about how you design an indicator.

The governance side of your mandate is of particular interest to me. Reworking the patient-domain data modelling meant defining quality rules applied at ingestion; reconciliation discrepancies dropped by 60%, and the catalogue that came out of it now serves all three analytics teams in the organisation.

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