How the classic template is structured for a data scientist

The built resume opens with a summary naming your specialisation — NLP, forecasting, recommender systems — and one deployed model outcome. Skills follow, grouped into languages, ML libraries, and cloud or ops.

Project and experience bullets name the problem, the method, and the measured impact. Publications and links close the page for research-adjacent roles; education sits last for industry roles.

Customise the structure to your target role. Research-adjacent positions welcome a publications section with links; commercial roles want deployment stories in the first screen. If your background is academic, translate: thesis becomes project, teaching becomes communication, and methods become the decisions they supported. One production deployment story — monitoring, drift, retraining, or business adoption — is worth more than any number of coursework models, so make sure at least one bullet tells it. Then keep the stack line ruthlessly current: data science tooling ages faster than any other section of this document.

Layout advice for data scientists

• Group skills by layer — languages, ML libraries, cloud and ops — directly under the summary.

• Each project bullet names the problem, the method, and the measured outcome.

ATS notes for data scientist applications

• Spell out "Machine Learning (ML)" and name libraries literally — scikit-learn, PyTorch, TensorFlow.

• Avoid two-column academic CV formats; they parse poorly in standard ATS.

Live preview: Data Scientist example in the classic template