Problem, method, result
For each piece of work, give the problem, the method you chose and why, and how you evaluated it.
RESUME EXAMPLE · DATA SCIENTIST
Data scientist resume example
A structure you can follow for a data scientist resume, with placeholders for your own details, plus the skills and tools mentioned most often in the 112 open data scientist listings on ROLIVA right now.
See open data scientist rolesCheck your resume against a job
What employers ask for right now
Python appears in 99 of 112 open data scientist listings on ROLIVA (as of ).
- Python99 of 112 listings
- SQL74 of 112 listings
- Machine learning73 of 112 listings
- Spark27 of 112 listings
- LLM22 of 112 listings
- AWS20 of 112 listings
- PyTorch19 of 112 listings
- A/B testing13 of 112 listings
- Scala11 of 112 listings
- Tableau11 of 112 listings
- BigQuery10 of 112 listings
- dbt9 of 112 listings
How this is counted: each open listing’s description and stated skills are checked, on whole words, against the same fixed skills vocabulary ROLIVA uses on its job pages and its monthly skills reports. A listing counts once per term. A mention is not a requirement, and the counts change as employers open and close roles.
Example structure
One column, standard headings and real text: easy for a recruiter to scan and for an application system to read. Fill each bracket from your own record, and delete any section you have nothing true to put in.
[Your name]
[City, Country] · [email address] · [phone number] · [LinkedIn or portfolio URL]
Summary
Data scientist with [number] years of experience applying [methods] to [problem area] in [domain].
Experience
[Job title] · [Employer] · [City] · [Month Year] – [Month Year or Present]
- Built a [model type] to [predict or explain] [target] using [data], evaluated with [metric] on [validation approach].
- Designed and analysed [experiment, e.g. A/B test] for [product or team]; [the result and how it was used].
- Productionised [model or pipeline] with [engineering team or tools], monitored by [method].
[Earlier job title] · [Employer] · [City] · [Month Year] – [Month Year]
- [Two or three bullets in the same pattern: your action, its scope and a result you can support]
Publications and projects
- [Title], [venue or repository], [year], [link]
Skills
[Skill or tool] · [Skill or tool] · [Skill or tool] · [Skill or tool]
Education
[Degree or qualification] · [Institution] · [Year]
What to stress in a data scientist resume
Deployment and use
Say whether a model was used in production or for a decision, and by whom.
Publications and projects
List papers, talks or public repositories with links.
Skills section
Statistics and machine-learning methods you have used, Python or R, SQL, experiment design, and the platforms you have deployed on.
Write evidence-based bullets
- Start with what you did. Open each bullet with a verb that names your own action: built, wrote, led, analysed, ran. Avoid “responsible for”.
- Add scope. Say for whom, how much or how often: the team, users, region or volume. Scope makes a duty into evidence.
- Show a result only if you can support it. A number is useful only when you could explain how it was measured. If you cannot, describe the outcome in words instead.
- Use the employer’s words where they are true. If a term in “What employers ask for right now” matches work you have done, use that wording in your skills and bullets. If it does not match your experience, leave it out.
Patterns for data scientist bullets
- Built a [model type] to [predict or explain] [target] using [data], evaluated with [metric] on [validation approach].
- Designed and analysed [experiment, e.g. A/B test] for [product or team]; [the result and how it was used].
- Productionised [model or pipeline] with [engineering team or tools], monitored by [method].
Next steps
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