Data Scientist CV Example & Writing Guide
ML engineer and data scientist with expertise in recommendation systems and NLP. 4 years in production ML.
Regional CV conventions
These examples share the same content worldwide. The header, length and personal-data rules change by region. Showing guidance for UK (CV).
Length
One page under 5 years experience, two pages maximum for senior roles.
Photo
Not expected. Leave the photo off unless the employer asks for one.
Personal data
Name, phone, email and location only. Do not include date of birth, marital status or nationality.
Good to know
UK CVs follow the same no-photo, no-personal-data rule as North America.
Sample Professional Summary
"Data Scientist with 4 years building and deploying production ML systems at scale. Specialisms in recommendation systems, NLP, and time-series forecasting. Comfortable owning full ML lifecycle from exploration to monitoring."
Example Achievement Bullets
- ✓Built recommendation model lifting click-through rate by 14%, directly impacting £8M in annual subscription revenue
- ✓Deployed real-time fraud detection model reducing losses by £2M/year with 99.2% precision at production scale
- ✓Mentored 3 junior data scientists, all of whom were promoted within 18 months
Core Skills
Recruiter tips for Data Scientist CVs
- Structure your CV around problem, solution, impact. Avoid listing model names without context.
- Distinguish between research and production ML experience, companies value both but need to understand which you're strongest in.
- SQL is still a core skill. Don't bury it, many DS hiring managers filter for SQL explicitly.
- If you have publications, patents, or Kaggle rankings, include them. They signal peer validation of your technical skills.
Common CV mistakes for Data Scientists
- ✗Listing algorithms without explaining why you chose them or what they achieved
- ✗No mention of data pipeline, deployment, or monitoring, 'built a model' is incomplete without understanding how it reached production
- ✗Omitting Python libraries, PyTorch vs TensorFlow, pandas vs Spark matters to technical reviewers
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