How to write a Data CV

The conventions below are the ones this field actually uses. They are what the builder applies when you pick Data / Analytics / ML, and what the rating checks a finished CV against.

What it must contain

Leave any of these out and the CV reads as incomplete to someone who hires in this field:

Technical Skills
Work experience — State the decision your analysis changed, not the tool you opened.

The order to put them in

Order is not cosmetic. What sits at the top is what gets read before someone decides whether to keep reading.

Professional summary
Technical Skills
Work experience
Projects
Education
Publications
Certifications
Achievements
Languages

Section by section

Projects — Include the dataset size and the measured result - accuracy alone means little.

What to put numbers on

The single most common reason a CV in this field reads as weak is that nothing in it is measured. These are the figures that mean something here:

accuracy · AUC · RMSE · lift · revenue · churn · rows processed · runtime

Verbs that carry weight

Openers like “responsible for” and “worked on” describe a job description rather than a person. In this field these do the work instead:

Modelled · Forecast · Segmented · Automated · Reduced · Identified · Validated · Deployed

Length

1 to 2 pages is normal for this field.

Build a Data CV See a worked example

Free, no account, and nothing is uploaded — the builder runs in your browser and the CV never leaves it.