Data scientist CV template: an example to customize
A data scientist CV rarely stands out through the length of its tool list. The recruiter — often a data lead or product hiring manager — looks for proof that your models changed a business decision: a prediction shipped to production, churn reduced, a process automated.
Moreau
Data Scientist — Python / Machine Learning
Profile
Work Experience
Data team of 8, customer retention scope.
- Churn scoring model shipped to production: -18% attrition over 6 months
- Automated feature pipeline (Airflow) cutting compute time by 70%
- Monthly results presentation to marketing teams
Omnichannel sales analysis for purchasing teams.
- Monitoring dashboards adopted by 5 product categories
- Customer segmentation (clustering) that shaped 3 targeted campaigns
Education
Certifications
Fictional example CV — every section is editable in the builder.
Use this example as your starting point
This example, on our Vertex template, shows how to connect technical skills (Python, ML, SQL) to measurable impact. Customize it in minutes.
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- ✓ From €2 — unlimited CVs, 24h to 30 days
- ✓ ATS-friendly PDF
What a data recruiter scans first
- ✓Projects shipped to production, not just exploratory notebooks
- ✓Quantified business impact: accuracy gained, cost avoided, revenue generated
- ✓A prioritized stack: Python/SQL at the core, ML frameworks mastered vs touched
- ✓The ability to communicate a result to non-technical stakeholders
- ✓GitHub, Kaggle or a project portfolio reachable in one click
3 tips to make your data scientist CV stand out
1. Show impact, not the model alone
“Scoring model deployed: -18% churn over 6 months” beats “tuned XGBoost”. The recruiter remembers the business decision, not the hyperparameter.
2. Separate exploration from production
State what reached production (API, pipeline, monitored dashboard) versus what stayed a POC. This distinction sets a junior apart from an operational profile.
3. Prioritize your stack
Python, SQL and one ML framework should dominate. Avoid listing 20 libraries at the same level: it dilutes real skills and blurs ATS matching.
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