8 templates · free, no signup
The usual failure on a data science CV is describing the model instead of the decision it changed. "Trained a gradient-boosted classifier" is a task; the saved review hours or the lift in a business metric is the result recruiters can use.
These data scientist resume templates give languages, libraries and deployment context a compact rail, and leave the timeline for problem, method and outcome. They are built so a keyword search for Python, SQL or the named cloud still hits.
8 layouts that fit how data scientist hiring actually reads a CV. Open a card to preview, or start in the builder — the same content reflows if you switch template later.
Data scientist resume template with ML stack, model metrics, datasets and deployed projects. Free technical ATS CV with instant PDF export.
Technical two-column resume with midnight sidebar and cyan accent for software engineers and developers. Tech-stack focused.
Research scientist CV template with publications, methods, grants, patents and conference evidence. Free ATS-readable format for R&D roles.
Scandinavian minimal resume with slate primary, icy blue accent and generous whitespace for engineers and product leaders.
Confident indigo banner resume with violet accent rules and pill skills for software engineers and data scientists.
Space-efficient dense resume with tight type, a blue accent bar and a skill grid, made to fit a long career on one page.
Timeless single-column classic resume with warm brown accents and centered header. Built for corporate, legal and business analyst roles.
Ultra-clean minimal resume with sky-blue accent, generous whitespace and a left-aligned header. Perfect for tech and product roles.
Layout, not adjectives
The right template for this field is the one that puts the evidence a data scientist screen checks first in a document a parser can still read. Style is secondary.
Match the market
What belongs on the page
Name the decision, the method once, and the metric that moved. Methodology essays belong in an appendix or the interview.
If a model is in production, say so. Notebook-only work is still valid early in a career; pretending it is a shipped service is not.
These two are the first search terms on most data science reqs. Write the names, not paraphrases.
"18 months of transaction-level data, 12 million rows" is enough. Never reproduce proprietary figures.
From template to file
Match the template to whether the role screens on shipped models or on publications.
Languages, libraries, data platform, visualisation. Recruiters scan for SQL and Python first.
If a bullet cannot name a decision or a metric, it is probably a task — cut or rewrite it.
Confirm that library names survived as text, then send. The ATS checker can run on the exported PDF.
Questions
Only beside the business outcome. An AUC on its own does not tell a hiring manager whether the model was useful.
A readable repository with a README that explains the problem, the data and the result helps. A notebook with no narrative is weaker than two lines of production work on the page.
Assume the first reader is a recruiter searching keywords and the second is the hiring scientist. Name the technique once, plainly, then describe the result.
Yes once you have several deployed projects or a publication list worth keeping. Compress older roles instead of deleting them.
Open a template, replace the sample history with your own, and export a PDF or Word file. No account.
