14 Data Scientist Resume Examples & Guide for 2023

Data science is a complex industry, and continues to evolve in today’s technological landscape.

Machine learning and ChatGPT may be booming right now, but it can be challenging to stay on top of these rapidly changing technologies.

Your data scientist resume needs to demonstrate your technical skills as well as your ability to communicate with others. Show the distinct value of each of your projects while avoiding redundancy.

Don’t worry, our guide will show you how to write an incredible data scientist resume that highlights your expertise in Python and SAS without overshadowing your interpersonal skills.

This guide will teach you:

• How to use our data scientist resume templates to make a good impression and attract recruiters’ attention.
• How to format your experience section so that hiring managers can see how you’ll impact success at their company
• How to showcase your skills in a way that shows you’re on top of industry trends and are the right candidate for your target job
• What recruiters look for and how to write a strong data scientist resume summary that gets callbacks

Looking for related resumes?

• Data Engineer Resumes;
• Entry Level Data Analyst Resumes;
• Tech Resumes;
• SQL Developer Resumes;
• Tableau Developer Resumes.

Data scientist resume example

How to format a data scientist resume

There are a few different resume formats to consider for your data scientist resume, but your best bet is to go with a reverse-chronological resume.

The focus of this format is on your work history listed in reverse-chronological order, just as the name suggests.

This is the best choice for senior data scientists who have been in the industry for 10 years or more. It brings attention to your career growth and shows your commitment to your work.

If you’re changing careers or just starting out as an entry-level candidate, a better choice is a functional resume format. This brings attention to your skills and away from your lack of experience.

When choosing a file format for your data scientist resume, always go with PDF. It provides extra security and ensures there won’t be any unwanted formatting changes.

Only use a DOC or another file format if the job application instructions explicitly say so.

As far as data scientist resume length, limit yours to one page. Recruiters don’t want to spend time reading through lengthy resumes, so stick to what’s relevant.

If hiring managers want to see more of your work, your GitHub link is there to guide them.

(CTA to ats checker - Enhancv to do)

How to create an impactful data scientist experience resume section

The experience section is the core of your data scientist resume. It’s where you’ll let all your hard work shine.

To make the most impact possible, follow these key rules:

• Include only major and relevant positions - the 2-month stint behind the counter at your grandfather’s banana stand interests no one. But that job as a data engineer working on sales data for a national fruit reseller is something the recruiter needs to see!
• Make it reverse-chronological - it’s the resume standard, and it saves mental energy for the recruiter. List your most recent positions first.
• Focus on impact rather than responsibilities - data mining, statistical analysis, and data visualization will be on almost every data scientist’s resume. Instead, explain the impact you had rather than just listing job duties.

Let’s take a look at a data scientist resume experience section to see how to avoid a common mistake.

What doesn’t work in this example:

• No quantitative metrics or measurable results
• Uses broad verbs like “did” and “coordinated” that don’t speak to success
• Leaves out industry-specific knowledge or skills

Let’s look at that example again with a few changes.

What works in this example:

• Shows evidence of specific results by “achieving a 20% improvement of approval decision time”
• Shows project management skills by mention “team of 20 data scientists working on 6 different projects”
• Shows industry-specific “data restructuring” skills and reach of “16 different countries”

This version is a big improvement. It quantifies impact with measurable results and industry-specific skills.

Always focus on relevant achievements instead of general responsibilities and tailor every section of your resume to fit your target job.

How to quantify impact on your data scientist resume

Companies hire data scientists to provide solutions and maximize success. If you want hiring managers to give you a chance, you need to quantify impact on your resume.

Recruiters will be looking through a stack of resumes that all list “data visualization” and “algorithm development” as skills. It’s not enough just to list it. You need to prove it.

Provide evidence to support your claims by sharing specific achievements with measurable success. Use real data and numbers to quantify impact in every section of your resume.

Quantitative data that can strengthen your data scientist resume include:

• Increased sales revenue
• Reduced redundancy or errors
• Rate of engagement or number of users
• Improved algorithm accuracy
• Profit margin
• Time saved for the company
• ROI for projects

Use these metrics throughout your resume to show potential employers exactly how you’ve achieved succes in previous roles.

Writing an entry-level data science resume

Just because you’re a recent grad looking for your first job in data science, don’t start thinking “I’m done, I don’t have any experience yet!”.

You’re mistaken if you think you don’t have any experience. Consider including

• Course projects that involved data science work - surely you’ve practiced your skills on a few practical exercises you can list here. Just make sure you feature the new and exciting projects - no one wants to see the same tired Titanic Survivor project!
• Internships - no matter if it’s your uncle’s company or a university help gig, you probably learned a lot, including keeping up with deadlines, working well with others, and communicating data results to different audiences. Practical skills matter, even if they’re soft skills.
• Volunteer work or side projects - if you don’t have practical experience, create some. There are tons of local SaaS startups that would benefit from logistic regression analysis to uncover their user activation points - help them out and use that as a practical example in your resume.

As you can see, there is a lot going on beyond traditional 9-to-5 steady job experience. And all of these will look great on your data scientist resume!

Looking to build your own entry-level job resume? Follow the steps in our guide on How To Write Your First Job Resume.

How to list your hard skills and soft skills on your resume

A data scientist needs a unique set of skills that lets you explore, transform, visualize and model datasets, and also communicate constantly with diverse stakeholder groups.

Make a good impression by showing that you have the right combination of hard skills and soft skills to accomplish this.

In “Top 10 Big Data Skills to Get Big Data Jobs” Amit Verma presents a comprehensive list of languages and systems data scientists should be able to work with, including

Make sure you include only things that you know well enough to start working with tomorrow. There’s no point in inflating expectations and then missing the mark.

What about soft skills?

Just knowing the technology won’t cut it, you need soft skills too. We list some great ones below, and you can check out KDnuggets list of important soft skills.

The world of data is complex. Demonstrate that you can navigate through it, but also help others orient themselves in it. Make sure you cover this, especially for more senior positions where presenting to managers is everyday work.

How to list your certifications and education on your resume

You’ve come a long way to becoming a data scientist. You’ve put in a ton of hours reading O’Reilly textbooks, debugging Python scripts, and creating visualizations in Tableau.

Make all your hard work show on your resume. For a stellar education section, add info on

• Your university and major
• Your GPA and final marks
• Key courses relevant to the position you’re applying for
• Any awards you received or societies you were part of

Since data science is a relatively new field, it’s common for professionals to come into it from different fields. If this is the case for you, you can shorten your education section and include additional courses and certifications you’ve earned.

Make sure you follow a few rules when presenting certifications on your resume:

• Make them stand out - don’t bury your certifications in another resume section, give them their own
• Add any capstone projects you worked on - certifications usually make you show what you learned in practice, prove that you can do what you say
• Show them your drive - if you completed the certification course quickly, mention it on your resume. It shows dedication and motivation to learn.

How to write your data scientist resume summary or objective

You may have heard the terms summary and objective used interchangeably when talking about resumes.

To get specific, a summary typically captures your industry experience and a few career highlights in 2-3 sentences. An objective talks about what you want to achieve in the future.

These days they are usually combined into one statement and referred to by either name.

A good formula for your data scientists resume summary is to write 2-3 sentences that cover the following points:

• Your title and role in the industry
• A top career highlight
• A shared goal of you and your potential employer

Let’s look at an example that uses this template.

What works in this example:

• Specific number of years of experience and industry focus
• Shares a measurable result that achieved “94% accuracy”
• States shared goal to “increase engagement with Python modules” with target employer

Additional sections for a data scientist resume

Depending on your experience and career path, there may be additional sections you want to include on your resume.

• Projects - including a section for projects can be key in increasing the value of a data scientist resume. Potential employers want to know how you’ve used your practical skills, and a successful project is a great way to show that.

• Awards - important industry achievements or competitive awards can be a great way to show your value. Include any relevant awards you’ve earned in the field.

• Volunteer work or hobbies - not all practical experience has to come from a job. There are plenty of ways you can develop relevant skills through volunteering or hobbies. Include any experiences that speak to your industry knowledge.

• Publications - a good data scientist is a clear communicator as well as a numbers person. Publications will highlight your ability to clearly communicate complex ideas.

Remember that publications aren’t just research papers published in peer-reviewed journals. This section can also include links to blog posts you’ve written to show that you can speak in more than just an academic tone.

Key takeaways for writing a competitive data scientist resume

To sum it all up, a great data scientist resume should tick these boxes:

• Make a good impression and show your body of work with links to your GitHub portfolio and LinkedIn profile
• Demonstrate practical knowledge and quantify impact with measurable results so hiring managers will know that you can achieve success
• Show how your skills align with the requirements in the job description by tailoring every section of your data scientist resume to your target job
• Include additional sections that show that you stay on top of industry trends and are the right candidate for the job

Now you’re ready to create your amazing data scientist resume and land an interview for your next job!

By Experience

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Data Science Intern

Breaking into the data science field calls for showcasing your understanding of data analysis and minor project experience. Highlight your knowledge in popular data science tools, languages, and libraries such as Python, R, Tensorflow, and PyTorch. Nonetheless, don't merely list these skills, demonstrate how they helped you in your projects or past internships, such as 'improved processing time with Python...'. Display statistical and mathematical expertise and be sure to include any relevant academic projects. Lastly, emphasize concepts like data cleaning, visualization, and machine learning models.

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Entry-Level Data Scientist

For entry-level data science roles, emphasize your academic qualifications, understanding of data science basics and readiness to learn. Include your knowledge of key programming languages like Python, R, SQL; tools like Tableau, Excel; and methods for machine learning and statistical analysis. Showcasing projects or internships, where you applied these skills, can be advantageous. For example, 'capitalized on a machine learning algorithm in a project that improved predictive accuracy by...' stands out more than mere statements about your knowledge level.

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Senior Data Scientist

As a senior data scientist, your resume should exhibit a strong background in data analysis, machine learning models, and leadership. Highlight examples where you've guided teams, managed projects, and made major impacts, such as 'led a team to develop a predictive model which improved revenue forecasting by...'. Mastery in Python, R, SQL, and experience with Big Data tools like Hadoop or Spark should be prominent. Don’t just list these experiences; correlate them with insights gained or improvements accomplished.

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By Role

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Machine Learning

As a Machine Learning applicant, you need to illustrate how your expertise in algorithms and neural networks have been put to practical use. Share experiences of using Python, R and TensorFlow to develop or improve machine learning models, following a pattern of 'skill-action-results'. Showcase any projects where you have used these skills to drive performance enhancements like 'improved accuracy of prediction model by...'. Highlighting strong mathematical foundation, knowledge of relevant ML concepts, and a sound understanding of data structures and algorithms could increase your chances.

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Python Data Scientist

Potential employers will be looking at your Python expertise along with how it underpins your work in data science. Highlight specific projects where Python was instrumental in acquiring, cleaning, analyzing, and visualizing data. Discuss how a Python script or library boosted efficiency. Don't simply list skills, demonstrate their practical application, like 'used Pandas to clean and structure large datasets leading to faster analysis' or 'employed Matplotlib for data visualization, enhancing interpretations.' Detail familiarity with Python-specific data science tools like NumPy, SciPy, Scikit-learn, etc.

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Associate Data Scientist

As an applicant for an Associate Data Scientist job, improving your chances involves highlighting your data modeling and statistical analysis skills, specifically how you've leveraged data to influence decision-making and strategy in past roles. Your familiarity with R and Python, common programming languages in the field, should be emphasized. Demonstrate your analytical thinking skills by explaining how they contributed to the success of your previous projects. Also, illustrate your experience with machine learning algorithms and predictive models, focusing on how you used these skills to effect positive changes previously.

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Data Science Manager

Essentially, when seeking a data science management position, display leadership experience in the data science field. Mention instances where your leadership or strategic decisions made an impact, like 'steered a 5-member data science team to enhance customer recommendation algorithm, raising click-through rate by...'. Show your proficiency in using a broad range of data science tools and how you've involved them in project management. Emphasize understanding of data, business acumen, and decision-making skills rather than simple technical knowledge.

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NLP Data Scientist

NLP Data Scientists typically have a foundation in natural language processing. It's important for your job application to highlight your deep learning experience, especially how you've applied these models for NLP tasks. Be sure to outline your skills in programming languages like Python, R, and Java and demonstrate how you've leveraged them for tasks such as text classification or named entity recognition in NLP. Highlighting any work using NLP libraries such as NLTK, SpaCy, Gensim, with concrete examples of their application in past projects will be beneficial. Finally, don't forget to mention your experience with machine learning, emphasizing how you've developed and applied these models within the scope of NLP.

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Metadata Scientist

As a Metadata Scientist, your key responsibilities include managing and improving the company's data portfolio. Essential qualifications for this role include experience in data modeling and database design, and a high level of skill in managing metadata within systems and databases. Being able to communicate effectively and manage stakeholders is also crucial, as you will often collaborate with various teams to ensure the integrity of metadata. Proven experience in the development and implementation of metadata management solutions is equally important, with emphasis on practical examples showcasing this skill.

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Educational Data Scientist

As an Educational Data Scientist, it's vital to highlight certain skills in your application. Including experience in educational assessment and survey design is useful, and familiarity with tools such as SAS, SPSS, or R is beneficial. Use concrete examples of how you've used such tools in solving educational problems. Detail any cross-functional collaborations you've had with educators or policy makers, which can show your ability to work with non-technical stakeholders. Also, emphasize any experience with educational research methods and qualitative data analysis.

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Data Science Director

As a Data Science Director, exhibiting strong leadership skills is important as is your data science proficiency. You should stress on your previous times when you effectively led a team to complete a project. You should also show your skills in several programming languages and tools by giving examples from your work on large projects. It is also key to share your past success in making strategic decisions and providing thought leadership with specific examples where you made significant improvements. Having certifications in data science, machine learning or similar fields can boost your application.

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Data Science Consultant

As a Data Science Consultant, you would combine business consultancy with data science expertise. For this role, it’s important that you have strong skills in data analysis and business intelligence. Having experience in combining these skills is especially beneficial. Being business-minded is also important, so providing examples where you used data analysis to boost business decisions or performance can be advantageous. Excellent communication skills are vital, particularly when explaining technical topics to those without technical knowledge. Remember to mention instances where this contributed to the success of a project. You should also be proficient in using statistical software like R, Python, and SAS.

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Data Analytics Scientist

As a Data Analytics Scientist, you'll turn raw data into useful insights. Make sure you show your skill with data analysis tools such as Excel, SQL, or R by giving actual examples of use. Stand out by having experience with predictive analytics, data mining, or machine learning. Prove your expertise in using visualization tools like Power BI, Tableau, or Looker to deliver data-based insights. Show how you've handled both structured and unstructured data by providing real examples from past roles or projects where you successfully transformed data.