CV Template
Data Scientist CV Template & Examples (ATS-Optimized)
Data Scientist hiring rewards candidates who connect rigorous modelling to business outcomes, not just notebooks full of experiments. Recruiters and ATS systems scan for the statistical toolkit (Python, pandas, scikit-learn, SQL) plus evidence you shipped models that moved a real metric. This template frames your projects so the parser catches every keyword and the hiring manager sees measurable impact.
Written & reviewed by the CVWon Editorial Team · Updated June 2026
Build Your CV NowTo write a strong Data Scientist CV, lead with Technical Skills, Professional Experience and Data Science Projects — each backed by specific, quantified results rather than generic duties. A strong Data Scientist CV ties every model to a quantified business result, such as a churn model that cut attrition 9% or a recommender that lifted revenue per session 14%.
ATS Optimisation
ATS Keywords
Include these keywords in your CV to pass applicant tracking systems.
A strong Data Scientist CV ties every model to a quantified business result, such as a churn model that cut attrition 9% or a recommender that lifted revenue per session 14%. The best candidates show the full lifecycle: framing the problem, engineering features, validating with proper experiment design, and deploying to production rather than leaving work in a notebook. Recruiters separate analysts from data scientists by depth in statistics, machine learning algorithms, and A/B testing rigour, so name your methods precisely. Weak CVs list tools without context or describe Kaggle projects as if they were production systems. Strong ones balance technical specificity (XGBoost, hyperparameter tuning, cross-validation) with clear stakeholder communication. The decisive differentiator is proving your models reached and improved real users.
Structure
What Sections Should a Data Scientist CV Include?
Technical Skills
ATS matches exact languages, libraries, and ML methods named in the job description.
Example
Python, SQL, pandas, scikit-learn, PyTorch, Spark, XGBoost, A/B testing, statistical modelling
Professional Experience
Hiring managers want production impact, not Kaggle scores, to gauge real-world ability.
Example
Built a churn model (XGBoost, AUC 0.89) that reduced monthly attrition by 9%, saving $1.2M ARR.
Data Science Projects
Demonstrates end-to-end problem framing, modelling, and deployment beyond your day job.
Example
Developed an NLP pipeline classifying 50k support tickets at 92% accuracy, cutting triage time 40%.
Education & Research
Validates statistical foundations and signals comfort with rigorous methodology.
Example
MSc Statistics; published paper on Bayesian hierarchical models (NeurIPS workshop, 2023).
Certifications & Tools
Cloud and ML certifications reassure recruiters you can operate at production scale.
Example
AWS Certified Machine Learning - Specialty; Databricks Certified Data Scientist Associate.
Avoid These
What Are Common Data Scientist CV Mistakes?
FAQ
Frequently Asked Questions
One to two pages. Prioritise three to four production projects with quantified outcomes over an exhaustive course list. PhD candidates may add a publications section but should still keep the core experience tight and metric-driven.
Yes, if they are strong, such as a top-10% finish, but label them clearly as competitions, not production work. Recruiters value them as signals of skill but weigh deployed, business-impacting models far more heavily.
Cloud ML credentials like AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or the Databricks Data Scientist certification carry real weight because they prove you can scale models in production.
Translate every model into a metric a stakeholder cares about: revenue, churn, cost, or time saved. For example, 'recommender lifted revenue per session 14%' resonates far more than 'achieved 0.91 AUC'.
No. Many data scientists hold a master's or strong portfolio instead. A PhD helps for research-heavy roles, but demonstrable production modelling, solid statistics, and clear communication of impact matter more for most industry positions.
Salary
Salary by Experience Level
Typical salary ranges by seniority (EUR, gross).
| Level | Experience | Salary range |
|---|---|---|
| Entry Level | 0–2 years | €35K – €55K |
| Mid Level | 3–5 years | €55K – €85K |
| Senior Level | 6–10 years | €85K – €130K |
| Lead / Manager | 10+ years | €120K – €170K |
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