24 Data Scientist CV Examples for 2026

How to Write a Data Scientist Resume

To land a data science role today by following the best practices, your resume must survive automated screenings, highlight quantifiable impact, and surface the exact skills recruiters value the most. Whether you’re tailoring your resume for a job at one of the top tech giants or a startup in your city, learning how to frame each line with precision makes the difference between getting filtered out and getting an interview invite.

Use proven resume examples to benchmark your formatting and content strategy, and test your draft with a free resume checker to see how it compares to the competition and performs in AI scans.

Key elements every data scientist resume should include:

• Professional summary
• Technical skills section
• Project highlights
• Work experience – Focusing on impact-first bullet points
• Education & certifications
• Publications or GitHub portfolio
• Keywords from the job description

What is the best resume format for a data scientist?

The best resume format for a data scientist in 2026 is the reverse chronological layout, which prioritizes your most recent and relevant roles and measurable impact. This format helps recruiters quickly assess your fit.

Follow these format tips:

• Stick to one page — Brevity is an asset here
• Use clear headers for each main section, such as “Contact Info,” “Work Experience,” “Project,” and “Skills.”
• Put the most relevant experience and results at the top
• Keep critical info (like GitHub links or major projects) visible and not at the bottom
• Avoid a functional format unless you’re making a career shift

What is the best resume template for data scientists?

The most effective resume template for data scientists is a clean, one-column layout optimized for scannability.

Template must-haves:

• Clear section heading (Projects, Experience, Skills)
• Bold job titles
• No graphics, charts, wild fonts, or headshots
• Room for GitHub/portfolio link

How to format your data science resume for ATS

Remember that ATS systems parse raw text, so what looks stylish to your eyes may be invisible to the bots. To ensure a real human recruiter reviews your resume, stick to these formatting rules:

Do:

• Go with simple and professional fonts (Arial, Calibri, Times)
• Save and submit in PDF unless directed otherwise
• Use clear and stand-alone headers: “Work Experience,” “Skills,” “Education”

Don’t:

• Use icons, emojis, or charts
• Submit scanned or image-based resumes
• Use tables, graphics, and multiple-column layouts

What do recruiters look for in a data science resume?

When recruiters review your data science resume, they’re looking for three things: impact, skills, and clarity.

In not more than the first 7 seconds, they typically scan for:

• A job title right under your name, in this case, Data Scientist
• Tools/skills/software that match the job description (Hint: Python, SQL, & ML)
• Quantified results from past relevant work or niche projects—Proof you can deliver results applying data
• A clean, readable design without walls of text

How to present your data science projects and work experience

Numbers are your most powerful persuasion tools. Quantify everything — even estimates with percentages, dollar amounts, frequencies, and timelines.

If you’re unsure how to go about this, ask: What changed after I performed this task? If the result can’t be quantified, it doesn’t add value to your candidacy.

Effective ways to showcase impact:

• Revenue: “…increased annual revenue by $250K”
• Efficiency: “…automated reporting to save 12 hours/week”
• Engagement: “…boosted clickthrough rate by 12%”
• Growth: “…led to a weekly average of 3.1K new sign-ups via optimized model”

The template for successfully discussing your experience as a data scientist is:

As a data scientist, emphasise your value by showcasing the quantitative impact of your work. These can be estimates. For instance, did you automate a report? Approximately how many hours of manual work did you save each month? Here are some ideas on how you can quantitatively discuss your projects:

How to define the impact of your data science work

Numbers draw attention, are convincing, and make your CV more readable. Which of these two ways to describe reporting is more compelling?

If nothing else, please take this away from this guide: state the results of your projects on your CV in numbers.

What are the trade-offs between projects and work experience?

Simply put, the more work experience you have, the less space “projects” should occupy as a section on your CV. In the sample CVs above, you’ll notice that only the more entry-level data scientist CVs have a section for projects.

The senior-level CVs focus on projects in the context of experience within companies. Space is valuable on a one-page CV, so you’ll want to focus on the bullet points that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

How to include entry-level data science projects for resume

Junior data scientists should include projects on their CVs. Try starting with a CV outline, where you can jot down anything and everything about your projects; then, you can refine the best of it into your final CV. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can demonstrate for entry-level data science projects, the better. Do you have any questions you’ve always wanted answered? You can probably think of some clever ways to gather data related to that question and come up with a reasonable answer. For example, our co-founder wanted to know which data science job boards were best, so he compiled some data, outlined his assumptions and methodology, and drew his conclusions.

No matter which projects you include on your CV, be sure to clearly state the question you were addressing, the tools and technologies you used, the data you used to address the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

What’s the difference between entry-level and senior data science resumes?

Rule of thumb:

If you have more than three years of experience, consider reducing the scope of your project section to focus on your key achievements. If you’re new, make it the shining star.

What about the data scientist resume summary?

Since you have limited space on your CV, you should only include a career objective if you take the time to customise it for each role to which you apply.

You may want to include a CV summary or objective when you’re making a significant career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalise it for each job.

Include the title of the job you’re seeking under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist roles, you would put “data scientist” under your name as the headline:

What skills belong on a data science resume?

Only include skills you’d be confident in and defend during an interview. Recruiters can tell “skill vomit” at a glance.

Even if you don’t have a college degree, your skills may get you hired—so you want to make the most of this section.

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb: if the skills section takes up a third of the page, it’s a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your CV is this: if it’s on your CV, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your CV. If you only have a handful of tools at your disposal, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all sorts of data science jobs available. Our scraper, which indexes jobs across thousands of company websites, shows over 5,000 full-time data science job openings in the UK across all levels of experience and skill sets. And our scraper has plenty of room for improvement, so that’s significantly lower than the actual number.

There are loads of fish in the job market sea; you just need a fishing rod.

Do list:

• Python, SQL, pandas, scikit-learn, matplotlib
• Key libraries: XGBoost, Dask
• Cloud tools: AWS, GCP

Don’t list:

• Buzzwords that you only know in theory
• Special skills, unless you’re fluent in them, are in demand in the job description

Junior vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should mention the major tools and languages you use but save specific modelling techniques for the “Work Experience” section. Demonstrate how you used particular models in the context of your work.

When you’re more junior, you probably haven’t had the opportunity to use all of the techniques you’re comfortable with in work or a project. That’s fine! It’s expected. However, you still want to make it clear to a potential employer that you can use those methods or libraries.

Essential soft skills for data scientists

While technical skills say you know the job, soft skills help you succeed by communicating and working with teams effectively.

Most-preferred soft skills in data science resumes:

• Communication: Presentations, delivering goals and results
• Problem-Solving: ask and respond to business questions in the best way possible
• Initiative: Do you use a proactive approach in finding insights?
• Collaboration: Can you team up with product, engineering, and marketing for common goals?

Don’t just list these skills; put them in context within your work bullet points as in the example below:

How to list education for data scientists

Education is quite similar to skills in that the more senior you are as a data scientist, the less space the education section should occupy on your CV. When you’re seeking one of your first data science roles, you might want to include courses related to data science to demonstrate you have a strong foundation.

Courses in subjects such as linear algebra, calculus, probability, and statistics, as well as any programming courses, are directly relevant to becoming a data scientist. If you’re looking for your first job after university, you should include your degree classification on your CV. Once you have a few years of work experience, it’s not necessary to include it.

If you have just completed (or are completing) a data science boot camp, this is where you should list it. You can include the relevant modules or courses you took. Make sure to include a few projects from your boot camp (especially if it was an original project) in the “Projects” section of your CV.

The education section should include:

• Field of study (e.g., B.S. in Computer Science)
• College/university’s name
• Years attended
• Location: city and state
• Relevant coursework (if entry-level)
• GPA (only if recent and >3.5)

Example:

How to add your contact information

The takeaway from this section is simple: this is not where you should make a mistake. Storytime! When our co-founder was first applying for jobs after university, he realised about 20 applications in, he had spelt his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is incorrect, your response rate from companies drops to zero per cent. That’s just maths. We’ve seen exactly four data science CVs where the email address on the CV was incorrect.

Ensure your email address is appropriate. While we don’t doubt the authenticity of your “[email protected]” email, perhaps avoid using it when applying for jobs. To be on the safe side, opt for a combination of your name and numbers for your email.

This is the section where you can include anything you want to showcase for a data science role. Do you have a blog where you document the analysis you conduct for Dungeons & Dragons? Are you active on Github or involved in an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

General CV formatting tips

This section is simply a list of one-off styling and formatting tips for your data science CV:

How to customize your resume for each job

You don’t need an overhaul, but a few edits to suit your resume for each role.

How to customize:

• Use the exact job title in your resume headline
• Let your projects use as many keywords from the job description as possible
• Fill your skills section with the tools that the potential employer lists
• If applicable in your case, showcase domain overlap in your bullet points(e.g., marketing, healthcare, fintech)

Pro tip: Create a Python-focused and R-focused version of your resume if you’re an expert in both and swap copies depending on the role.

Here are the steps we recommend to customize it for each job:

Let’s walk through a specific example to illustrate what we mean by including particular projects for different roles. Let’s say that a senior data scientist is applying for the position below.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate played a significant role in developing data pipelines in his hypothetical role as a senior data scientist at EdTech Company.

So all we need to do is amend that section of his experience at EdTech Company to discuss that project, as you can see below:

How to customize your data science resume for startups

For early-stage start-ups (anything fewer than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal guidance.

Resume tips for data science bootcamp grads

For a bootcamp graduate, lean more on your projects for credentials.

To showcase them well, include:

• Bootcamp name, when, and where
• Relevant tools/languages learned
• Capstone project with clear impact—results, audience, and tech used
• GitHub

Example project bullet:

• Built a movie recommender system using collaborative filtering in Python with Scikit-learn, which saved 22 minutes per session and reached 96% satisfaction in user tests.

Common mistakes to avoid on a data scientist resume

Regardless of how strong a candidate you are, you can lose interviews over these avoidable mistakes:

• No quantified results
• Stuffed skills section
• Inconsistent formatting
• Wrong or missing contact info
• Generic content without focus on the specific role

Final thoughts

There you have it—a compelling, easy-to-read data science resume built for 2026. Now you can celebrate by doing something as fun as writing a resume. Maybe your taxes? Or go to the dentist?

By building or updating your current CV, you have taken a significant step towards securing your next (or first) data science job. Now, please, we urge you, check your grammar and spelling once more and have someone else read your CV. Don’t let that be the reason you don’t get an interview.

Congratulations! The first and hardest step is done. You have a data science CV! With great power comes great responsibility, so go and apply wisely.