Data Scientist
Best for candidates with 3+ years of experience
Resume Builder
Like this template? Customize this resume and make it your own with the help of our Al-powered suggestions, accent colors, and modern fonts.
Build my resume We’ve reviewed countless data scientist CVs and have made a concerted effort to distil what works and what doesn’t about each of them.
Our top tip for creating an effective data science CV is to quantify your impact on the business! The 22 data scientist CV samples below and our data scientist cover letter templates can help you build a great job application in 2025, no matter your career stage.
Whether you’re looking for your first job as a junior data scientist or are a veteran with over 10 years of expertise, you’ll find plenty of tools to build your perfect CV, like our new Word resume examples or free Google Docs resume templates.
Why this CV works
- We can’t stress it enough. Quantify your impact!
- You need to write your resume in a way that shows the employer you’ve made a significant impact on the companies you’ve worked for.
- This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
- The numbers on your data scientist CV can be rough estimates.
- They’re a way to quickly showcase your achievements and persuade the employer that you’ll bring that same level of energy to their team or company.
Why this CV works
- Your senior data scientist CV can really impress when you demonstrate a clear career progression from data analyst to data scientist to senior data scientist.
- That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 per cent of the page.
- If you have an impressive 10+ years of experience, you can choose to fill some of that remaining white space with a resume summary.
- A worthwhile summary should provide a quick overview of your career highlights in two to three impactful sentences and mention the target company by name.
View more senior data scientist resumes>
Why this CV works
- If you don’t have any specialised courses under your belt, then it’s time to bring out quantified bullet points from your previous roles.
- Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, ensure you quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualisation CV.
Why this CV works
- When seeking a data science manager role, it makes sense that your work experience focuses on leadership and project ownership.
- Once more, the outcomes of your work should be clearly stated in terms of tangible impact (are you noticing a pattern?).
- While content is king, appearance is queen.
- Using a two-column layout for your data science manager CV allows more information to fit on a single page. Even with over nine years of experience, keeping your CV to one page is ideal.
- Worried about these details? Our resume templates for 2025 might suit your specific needs; additionally, we’ve got fresh and free Google Docs resume templates that can make your resume-creation blues disappear!
View more data science manager resumes>
Why this CV works
- The recruiter is captivated by your beautifully formatted CV, and even without much experience, you’ve quantified your successes so well you’ve left them speechless. But you’re still not the well-rounded candidate they want, so there goes your dream role.
- To create an impressive data science student CV, showcase a diverse skill set, prioritising in-demand options (such as Python, Jupyter Notebook, Pandas, Excel, SQL Server, etc.). Soft skills, ranging from teamwork and leadership to problem-solving, creativity, and adaptability, are a welcome addition to your document.
Why this CV works
- Companies don’t expect their interns to have extensive industry experience, so it’s not essential to fully align your CV with your target role. However, if you have relevant skills that you gained outside of your work experience, a career objective is an excellent place to highlight them.
- Highlight your expertise in computer science by listing your proficiency in advanced programmes like Keras on your data scientist intern CV.
Why this CV works
- As someone who makes the decisions, you know things can quickly go awry after one wrong move, no matter how minor. Failing to proofread your data science project CV will have the exact same impact on your job search, so we recommend doing everything possible to eliminate those pesky grammatical mistakes.
- A free sentence checker like Grammarly can be handy here. Run your sales pitch through it, and voilà—grammatical errors like typos and run-on sentences come to light. Of course, a fresh pair of eyes can help, too, especially with those tricky errors a programme might miss—like “[email protected]” instead of “[email protected].”
Why this CV works
- A former Google recruiter once said, “A resume is showcasing you in a 10-to-60-second format on paper.” Creating a strictly text-based Google data scientist CV that doesn’t convey any achievements is only going to put off potential employers.
- To ensure you have enough space to tell your story, use our elegant template and include subtle colours that are stylish yet don’t distract readers. Remember to use appropriate spacing and highlight your best achievements to keep hiring managers engaged with your career narrative.
Why this CV works
- Remember, all experienced professionals have unique traits or skills that make them successful in their field, and data scientists are no different.
- Avoid mentioning basic tasks like extracting information from your experienced data scientist CV. Instead, use phrases such as “reduced maintenance costs” or “enhanced sentiment analysis accuracy” to demonstrate how you add value to businesses and succeed for any potential employer.
Why this CV works
- As a data science director, you will have a lot of experience under your belt. Remember that it’s not necessary to add every single thing you’ve ever done to your CV.
- For an effective data science director CV, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant achievements at the top, making it the first thing a recruiter will look at.
Why this CV works
- If you’re going to secure that appealing opportunity, you must demonstrate your software skills (think Python, TensorFlow, Apache NiFi, and Git). And one of the best ways to do that? Include a link to your GitHub profile in your data science engineer CV right now.
- Ideally, the link to your GitHub should go right in the header, providing a window into those relevant projects you completed but might not get a chance to highlight in your sales pitch. Then, instead of writing something like “github.com/yourname”, just write out the name (GitHub) and hyperlink as Ava does.
Why this CV works
- Your work experience is the most crucial component of your data science consultant CV.
- To best showcase your abilities, use metrics to discuss your achievements.
Why this CV works
- When you’re trying to work out what to put on your resume for a more specialised role like an NLP data scientist, it’s important you demonstrate your proficiency in operationalising models to make a significant impact on the business.
- Remember, the aim of any data scientist (including NLP specialists) is to deploy models to make positive impacts on product and user experience.
- Don’t focus on the technical aspects of the models you’ve built on your NLP data scientist CV (you’ll discuss those in more detail during your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.
View more NLP data scientist resumes>
Why this CV works
- Even if you already have considerable experience in your field, you can give your data scientist machine learning CV a competitive edge by highlighting your higher education. Make room to showcase your advanced degree in a relevant subject like statistics to further stand out.
Why this CV works
- What’s your track record in being a project leader and ensuring that all objectives are met within the specified timeframes? Potential employers are looking for someone with a history of consistent success.
- Mentioning achievements such as improving project outcomes and reducing process duration in your Python data scientist CV is a great way to capitalise on your experience honed over years of hard work.
- Then, by writing a great cover letter, you give yourself the opportunity to elaborate on exactly how you reduced the process duration as a Python data scientist.
Why this CV works
- Some people have few jobs in their experience sections, and yours might have next to none if you’re just starting out in your field. But don’t worry! You can make any internship or noteworthy project stand out by paying extra attention to your skills section.
- When you have little to no professional experience, the skills you list on your resume matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn.
- Your “beginner” resume is also the perfect place to include a resume objective statement that shows your ambition.
- When crafting your associate data scientist CV objective, immediately delve into any educational achievements or internship highlights with notable companies like Northrop Grumman. Then, add a touch of personality that demonstrates your enthusiasm for acquiring new knowledge—drive and curiosity are highly desirable traits in new professionals.
Why this CV works
- Considering adding projects to your entry-level data scientist CV instead of sufficient work experience?
- Great idea! Just make sure to discuss your projects in terms of their quantitative impact.
- You can demonstrate the impact of a project by posing a question and then answering that question with data.
- Once again, your results should consistently be expressed in numbers. Even if the result is as trivial as saving 12 minutes per film, it acknowledges the importance of measuring impact.
- Finish your piece with a resume objective only if you’re prepared to tailor it to the role you’re applying for. Tailoring involves: mentioning the target company by name and including relevant keywords from the job description.
View more entry-level data scientist resumes>
Why this CV works
- Know what’s better than having one educational qualification on a healthcare data scientist CV?
- Having two qualifications! Now’s the time to showcase all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare sector while the other highlights your expertise in data science!
Why this CV works
- What makes your Amazon data science CV stand out from the rest of the pile? Well, several factors should, but it’s the career objective that serves as your first line of “attack”.
- Let that statement encapsulate your aspirations and what you wish to bring to your new employer. Hiring managers are keen to see your passionate side and the value you add to the team.
Why this CV works
- The first thing recruiters will notice when reading your CV is whether the essential qualifications to perform the job adequately are there.
- Your data scientist, analytics CV should target the list of requirements that companies in your region commonly request.
- For example, 18 out of 20 job descriptions for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
- Once you’ve added job-market-specific data, our free resume review can evaluate your CV for other key elements such as spelling, grammar, and active language.
Why this CV works
- Talk about a great career summary! If you decide to include a resume summary in your educational data scientist resume, dazzle the reader with solid achievements that speak to their own relevance within the field.
- Consider being “well-rounded” as you write; you might include an exciting publication related to the job role, briefly outline your relevant experience or skills, and conclude with how and why you’ll enhance the company through your new role.
- Use your resume to not just list what you’ve done in previous jobs, but articulate how you bettered your previous work environments.
- Skills and qualifications add credibility, but potential employers also want to know about your impact.
- If you conducted assessments, what improvements did you implement afterwards? If you incorporated machine learning, what optimisations did you use it for?
Why this CV works
- Entry-level metadata scientist positions can be difficult to secure. However, your metadata scientist CV can be an excellent way to showcase large-scale data manipulation skills required by the industry.
- Demonstrate your experience in programming, testing, modelling, and data visualisation through well-designed projects that solve real problems through code.
- The key isn’t to reinvent the wheel but to create something dynamic and unique that can’t be easily replicated with a few Google searches and a video tutorial.
- Though related work experience is preferred when you make a resume, entry-level candidates often share a common denominator: a light or nonexistent job history in the field.
- Solve this problem with projects. If you’ve worked on excellent projects that utilised and showcased the necessary skills required for the job, list them and watch your CV blossom with confidence!
Why this CV works
- As a finance data scientist, your dashboards aren’t just attractive visuals—they’re your career pitch, and enhancing them with quantified metrics demonstrates your ability to turn numbers into profit.
- Think of it as your financial mic drop moment. Instead of saying, “I made some cool charts,” hit them with “Increased annual ROI of angel investments by 5.3%.” Not sure what makes such points stand out? They’re direct, concise, and specific. Just what employers want to see in a finance data science CV.
Why this CV works
- Not sure how to craft a compelling data science CV with 2 years’ experience? Let your career growth speak for itself. You didn’t go from “Hello, World!” to neural networks overnight, did you?
- Begin your journey as a junior statistical analyst grappling with Excel, progressing into a skilled analyst who has mastered detection algorithms, and now a data scientist developing highly accurate forecasting models. If anything, it demonstrates that in just two years, you’ve transformed raw data into pure gold.
Related CV guides
Writing Your Data Scientist CV
Recruiters only spend an average of seven-plus seconds reviewing your resume, so it’s vitally important that you catch their attention in that time. Our guide for 2025 takes you section by section through your CV to ensure you get that first interview.
You can successfully choose a winning resume format in 2025 that will capture an employer’s attention.
Pressed for time? Here are the quick summaries of each section you can apply to your CV:
- Projects & Work Experience
- Whether for a company or yourself, what you’ve worked on should be the focus of your CV. Always try to include a measurable impact of your work.
- Summary/ Headline/ Objective
- Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
- Skills
- Only include technical skills that you’d be comfortable using to code during an interview. Avoid a long list of different skills.
- Education
- Include relevant courses if you’re looking for a graduate role. Otherwise, make your work the focus of your CV. If you attended a training programme, list it here.
- Contact Information
- Double-check everything. This is not where you want to make a mistake. You don’t need to include your exact address. Town, county, and postcode are fine.
- General Formatting Tips
- Try to keep it to one page. Keep your bullet points concise. Triple-check your grammar and spelling, and then have someone else read it.
- Customisation for Each Job
- Read the data scientist job description. See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your CV.
Your data science projects and work experience
Let’s dive straight into the good stuff and discuss the most important part of your CV: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your CV decides whether or not you’ll be invited for an interview.
When discussing your previous work (whether that’s for another employer or on a side project), your aim is to persuade the person reviewing your CV that you’ll add value to their company. This is not the time to be modest. We want to see the kind of confidence you have when you’re wearing your favourite outfit.
The template for successfully discussing your experience as a data scientist is:
- Clearly state the aim of the project
- Show what you did
- You can mention the programming languages you used, the libraries, modelling techniques, data sources, etc.
- State the quantitative results of your project
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 for how you can quantitatively discuss your projects:
Ways to define the impact of your data science work
- Increased revenue
- Example: You developed a pricing algorithm that resulted in a £200k increase in annual revenue.
- Improved retention or conversion rate
- Example: You developed a model to predict who would cancel their subscription and implemented an intervention to improve monthly retention from 90% to 93%.
- Increased growth
- Example: You developed a marketing attribution model that enabled the company to concentrate on effective marketing channels, resulting in 2,100 additional users.
- Improved engagement rate
- Example: You conducted an experiment across different product features, which resulted in a 25% increase in engagement rate.
- Saved labour
- Example: As a side project, you developed a film recommendation engine that now saves you 26 minutes each time you need to decide which film to watch.
- Increase in customer satisfaction
- Example: Since you developed a customer segmentation model to determine how to communicate with different customer types, customer satisfaction has increased by 17%.
Numbers draw attention, are convincing, and make your CV more readable. Which of these two ways to describe reporting is more compelling?
- Used Python, SQL, and Tableau to carry out daily reporting for the business
- By using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work each week.
If nothing else, please take this away from this guide: state the results of your projects on your CV in numbers.
Balancing 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.
Entry-level data science projects for CV
Junior data scientists should include projects on their CVs. Try starting with a resume 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 show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to gather data around 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, laid out his assumptions and methodology, and drew his conclusions.
No matter which projects you include on your CV, ensure you clearly state the question you were addressing, the tools and technologies you utilised, 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.
The data scientist overview
Since you have limited space on your CV, you should only include a resume objective if you take the time to customise it for each role to which you apply.
You may want to include a resume summary or objective when you’re making a significant career change. If you do include one, ensure it is specific about your goal and experience. This is valuable space you will be using for 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:
Skills that bring in the money
The most common mistake we see on data science CVs (that we used to make on our own CVs) is what we call skill overload. It’s a long list of skills in which no one person could truly be proficient. A quick rule of thumb: if the skills section takes up a third of the page, it occupies too much space. This is a major warning sign for hiring managers.
The reason people create such a comprehensive skills section is to get past the so-called data science CV keyword filters. If you’re making small adjustments to your CV for each job you apply for (for example, include Python for roles that mention Python and R for positions that list R if you know both), you’ll have no trouble 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.
Skills sections: entry-level vs. senior
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 main 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 the techniques you’re comfortable with in work or a project. That’s fine! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.
Education
Education is quite similar to skills in that the more senior you become 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 like linear algebra, calculus, probability, and statistics, as well as any programming courses, are directly relevant to becoming a data scientist. If you’re seeking 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’ve just completed (or are completing) a data science boot camp, this is the place to list where you attended. You can include the relevant lessons or courses you took. Make sure to have a few projects from your boot camp (especially if it was an original project) in your CV’s “Projects” section.
Contact details
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 wrong, your response rate from companies drops to zero percent. 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 question the authenticity of your “[email protected]” email, it might be best not to use 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 do 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:
- Keep it concise. Bullet points should be informative but should not go on for paragraphs.
- Each bullet point in your CV should be a complete thought. You don’t need to have full stops at the end of each bullet.
- Maintain consistent tense usage. If you’re discussing previous projects in the past tense, ensure you do so for all past projects.
- Please, please don’t get your contact details wrong.
- Use Grammarly or a similar tool to check the spelling and grammar in your CV. Then do that two more times. Finally, have someone else review it just for spelling and grammar.
- Don’t give the person reviewing your CV a silly reason to put it in the “No” pile. Check your resume carefully.
Customisation for each application
You don’t need to go overboard with customising your CV. Here are the steps we recommend to tailor it for each job:
- Ensure that for each language you have extensive experience in (such as Python and R), you have separate CVs highlighting specific projects in each language.
- So in this example, we’ll have one “Python” CV and one “R” CV depending on what the job is seeking.
- Read the data scientist job description. Do any specific projects you worked on come to mind as you read it? If so, include those projects as bullet points on your resume. Here are some sample questions to help you think of specific projects to list for different jobs:
- For example, if you have experience with attribution modelling and this is a marketing data science role, you should include that experience.
- Do you have experience with a particular library or modelling technique they mention?
- Do you have experience in the field of the specific job?
- Do you have any relevant industry experience with the firm?
Let’s go 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 part in developing data pipelines in his fictional role as a senior data scientist at EdTech Company.
All we need to do is amend that section of his experience at EdTech Company to discuss that project, as shown below:
Data science CV customisation example
Original bullet on the CV: Worked closely with the product team to build a production recommendation engine in Python that improved the average time spent on the page for users and resulted in £325k in additional annual revenue
Customised for the role: Developed our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting and monitoring
Customisation for start-ups
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.
If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your CV. Include active words like “led” or “constructed” instead of passive language like “worked on” or “collaborated on.” We know this seems fussy, but this matters to early-stage companies. Hiring managers at companies of this size are pressed for time and will use any signal to filter candidates.
Final thoughts
There you have it—a compelling, easy-to-read data science CV built for 2025. Now you can celebrate by doing something as fun as writing a resume. Maybe sorting out your taxes? Or visiting the dentist?
By building or updating your current resume, you took a huge step towards securing your next (or first) data science job. Now please, we urge you, check your grammar and spelling again 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.
Secure your next job with our AI-powered, user-friendly tool.
Eliminate the guesswork in your job search. Upload your existing CV to check your score and make improvements. Create a CV with one of our eye-catching, recruiter-friendly templates.
• Work in real-time with immediate feedback and tips from our AI-powered experience.
• Utilise thousands of pre-written, job-specific bullet points.
• Edit your CV in-line like a Google Doc or let us guide you through each section one at a time.
• Enjoy peace of mind with our money-back guarantee and 5-star customer support.
