Real template preview
Statistics and experiments
Python / SQL
Decision-led modelling
CV Example
Data Scientist CV Example
This data scientist CV example shows how to present statistical modelling, experimentation, and practical insight in a way that feels useful to both technical and commercial stakeholders. It is built for candidates who need a template that proves analytical judgement, model quality, and measurable impact without reading like a generic data inventory.
Start with this data science structure, then tune the summary and top bullets around the experiments and models you want employers to notice first.
CV preview
Review Maya Collins's data scientist CV layout
This printable preview shows how Maya Collins presents Data Scientist experience in London, UK, with statistical modelling, experimentation, and practical insight made easy to scan.
The first page quickly signals fit through evidence such as experiment readouts, churn modelling, and forecasting work that changed product and commercial decisions.
Notice how the layout keeps Python, SQL, and statistical thinking visible while still leaving room for model quality, communication, and business impact.
Why it works
Why this Data Scientist CV example works
This data scientist CV works because Maya Collins's modelling, experimentation, and business impact are easy to place from the top of the page.
The remit is specific straight away
The summary makes it clear that this is a data science role rooted in modelling, experimentation, and forecasting rather than generic analytics language.
The evidence is decision-led
The experience bullets show product tests, churn modelling, and planning support in context instead of listing tools without a reason.
Projects add useful depth
The project section shows experiment and forecasting examples that strengthen the main employment history without crowding the page.
The skills section stays focused
Only the tools and practices that matter to data science hiring teams are included, which keeps the page easier to scan.
The format stays ATS-friendly
Standard headings, concise bullets, and a clear layout make the document straightforward for recruiters and screening systems.
Writing breakdown
How to write a Data Scientist CV
Use this data scientist example to see how statistical work, experiments, and machine learning evidence can be translated into a sharper summary, stronger bullets, and a skills section that stays focused.
1
Lead with the kind of data science work you actually do
Say whether the role is more experimentation-led, modelling-led, forecasting-led, or product-led so the CV is positioned correctly from the outset.
2
Connect methods to outcomes
A data scientist CV is stronger when each model or test is linked to a business decision, product change, or planning improvement.
3
Show model quality carefully
Mention metrics, validation, or evaluation where they are meaningful, but keep the explanation grounded in what the result changed.
4
Use projects to show breadth
A well-chosen project can prove experimentation, forecasting, or feature engineering more clearly than a long list of tools.
5
Write for humans first
Keep the CV easy to read for product managers, analytics leads, and hiring managers who want the story behind the model, not only the code.
Recommended skills
Skills shown in this data scientist CV example
A data scientist CV should show analytical depth, statistical judgement, and practical business impact. Focus on modelling, experimentation, and how your analysis changed decisions.
Role-specific skills
Python
SQL
Statistics
Machine learning
Experiment design
A/B testing
Pandas
Scikit-learn
Forecasting
Data visualisation
Working strengths
Analytical thinking
Communication
Curiosity
Problem solving
Organisation
Commercial awareness
FAQs
Frequently asked questions
These questions focus on modelling depth, experimentation, Python, and how to tailor a data scientist CV without turning it into a generic analytics profile.
What should a data scientist CV include? Open
Include a concise summary, relevant modelling and experimentation experience, core technical skills, and examples that show how your analysis changed decisions.
How do I show machine learning on a data scientist CV? Open
Explain the problem the model solved, the methods you used, and the outcome it supported. That makes the machine learning work feel useful rather than abstract.
Should I include projects on a data scientist CV? Open
Yes. Projects can be especially helpful when they show experimentation, forecasting, feature engineering, or model evaluation more clearly than work history alone.
How long should a data scientist CV be? Open
One or two pages is usually enough, depending on seniority. Keep the detail tight and prioritise the work most relevant to the role you want.
How do I make a data scientist CV ATS-friendly? Open
Use clear headings, avoid complex layout tricks, and include the technical terms that honestly reflect your experience, such as Python, SQL, modelling, and experimentation.
What skills should I put on a data scientist CV? Open
Focus on the tools and methods you use confidently, such as Python, SQL, statistics, machine learning, experiment design, and data visualisation, plus the communication skills needed to explain findings.
Start building
Turn this data scientist CV into your own
Start in Modern CV with this data science layout, swap in your own models, experiments, and metrics, and shape the final version around the analytical work that best fits the role.
Build once
Tailor each application
Export polished PDFs
Share live CV links
Helpful if you want a stronger data science first draft without starting from a blank page.
Inside Modern CV
Replace the sample profile, publications, grants, and teaching with your own evidence.
Create tailored versions for PhD, postdoc, fellowship, and university applications.
Export a polished PDF or publish a live link when you want a shareable version.