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Awesome Data Scientist Resumes [+ Tips & Templates]
Data Science

7 Awesome Data Scientist Resume Examples [Tips & Templates]

19 minute read | December 20, 2023
Sakshi Gupta

Written by:
Sakshi Gupta

Ready to launch your career?

In the dynamic field of data science, making a strong first impression is crucial, and it all starts with your resume. Did you know a recruiter spends an average of just 7.4 seconds reviewing a data scientist resume? In this comprehensive guide, we’re here to help you make every second count with your data scientist resume. We’ll share essential tips and insights on creating a resume that not only catches the eye but also showcases your unique skills and experiences in pursuit of your dream job.

Let’s get started!

How Important is a Data Scientist Resume?

Your data science resume is extremely important. The entry point to any position you want to be considered for is the data scientist resume. As the first step, organizations will expect applicants to submit a resume, cover letter, and data science portfolio. Without a strong data science resume, it would be impossible to even begin your job search. However, a data scientist resume can help you in a lot more ways than that.

  • Finding the Right Role: Your data scientist resume can help you find the right role, especially in the rapidly growing field of data science. According to the U.S Bureau of Labor Statistics, opportunities in data science are expected to grow by about 28%—roughly 11.5 million new jobs will open in 2026. However, there is an evident supply gap. In 2020, they found there were three times more job postings than job searches in the field of data science. With the right data scientist resume, you can position yourself for jobs that are a perfect fit for your skills and experiences.
  • Making a Great First Impression: Customizing your data scientist resume for specific roles and highlighting your projects can help make a strong first impression. This is important in a competitive field where many applicants may vie for the same position.
  • Standing Apart from the Crowd: A clear and impactful data scientist resume differentiates you from others. Emphasizing a unique combination of skills and experience helps make a lasting impression, beyond just technical competencies.
  • Driving the Interview Conversation: The information included in your data scientist resume can steer the conversation during interviews, as hiring managers often ask questions based on it. This helps in starting your career as a data scientist.
  • Negotiating Competitive Pay: While indirectly impacting pay, a comprehensive data scientist resume serves as a testament to your qualifications, ensuring that job offers reflect your value as a data scientist to the employer.
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Data Scientist Resume Examples

Entry-Level

In this section, we’ll guide you through creating an effective data science fresher resume, highlighting the crucial elements that fresh graduates or new entrants to the field should include to stand out.

What To Include

Organizations hire fresh graduates more on their potential than their past. Therefore, your resume needs to clearly and confidently demonstrate what you can do for the organization hiring you. To do this, include:

  • Data science skills you have
  • Projects you’ve done on your own or as part of a course/bootcamp
  • Certifications in data science or related subjects
  • Internships you’ve completed
  • Any pro bono or volunteer work you might have done applying your data science skills

Entry-Level Example 1

data scientist

Why is this resume good?

  • The career objective clearly states the candidate’s understanding of their skills and their needs
  • The relevant courses section highlights knowledge they have gained in college
  • The project section shows practical experience

Entry-Level Example 2

entry-level data scientist resume

Why is this resume good?

  • Summary is clear about experience and core skills
  • Experience focuses on responsibilities, demonstrating business context understanding
  • Skills section can be used to mirror the job description, making it easy for recruiters to make decisions

Senior Level

In this section, we focus on crafting a killer ‘Senior Data Scientist Resume.’ This includes tips and examples on how to emphasize your extensive experience, leadership skills, and advanced technical capabilities, tailoring your resume to reflect the depth of your expertise in the field of data science.

What To Include

As a senior data scientist with experience, you would be aiming for a position with more responsibility, like a data science manager, for example. This demands a customized and confident resume. 

  • Customize the resume for the job you’re applying to—highlight relevant skills/experience, mirror the job description
  • Focus on responsibilities and accomplishments instead of tasks
  • Include business outcomes you’ve produced with your work
  • Present case studies of your key projects

Senior Level Example 1

senior data scientist resume example

Why is this data science resume example good?

  • The information in this data science resume example is organized in a clear and concise manner giving the entire view of the candidate’s career without overwhelming the reader
  • Each job has quantifiable outcomes, demonstrating the business acumen of the candidate
  • Also subtly hints at leadership skills by mentioning the responsibilities taken in coaching and leading teams

Senior Leve Example 2

data science resume

Why is this resume good?

  • The focus of the resume is the experience section, outlining growth over seven years
  • The resume shows a clear progression from the post of a junior data scientist to a senior data scientist
  • Links to LinkedIn, GitHub, Medium, etc., show thought leadership

Other Data Resume Examples We Love

Now that you’ve seen the basic set of resume examples let’s explore a few others that might be equally effective. In this section, we’ve put together some non-traditional resumes and career paths for you to take inspiration from.

Example 1

data scientist resume example

Why is this resume good?

  • For an entry-level data scientist position, the resume focuses on educational qualifications, certifications, etc.
  • The projects section gives details about the goals, tasks, and tools used to demonstrate skills in practice

Example 2

data scientist resume header

Why is this resume good?

  • Within data science, the candidate demonstrates specialized skills
  • The work experience outlines the impact the candidate had created in the previous roles than focusing on the technical aspects of the model
  • Experience section demonstrates career growth

Example 3

data scientist resume template example

Why is this resume good?

  • Profile is clear about the experience and the role the candidate is seeking
  • Skills are listed clearly, making it easy to skim
  • Certifications listed prominently

More Examples

If you are still stuck, try these resume examples. Use the format to create a resume template you can populate on your own. Don’t forget to include a brief resume summary, highlighting your skills in machine learning, data visualization tools, and analysis according to the details of the data science job you’re applying for.

Senior Data Scientist Template

Jane Doe 123 Main Street Anytown, CA 12345 (123) 456-7890 jane.doe@email.com

Resume Summary

Senior Data Scientist with 7+ years of experience in developing and implementing machine learning models to solve complex business problems. Proven ability to lead and mentor teams, communicate effectively with stakeholders, and deliver high-quality results on time and within budget.

Skills

  • Programming Languages: Python, R, SQL
  • Machine Learning Algorithms: Supervised learning (linear regression, logistic regression, decision trees, random forests, gradient boosting machines), unsupervised learning (clustering, PCA), natural language processing (NLP), deep learning
  • Big Data Technologies: Hadoop, Spark, Hive
  • Cloud Computing Platforms: AWS, Azure, GCP
  • Data Visualization Tools: Tableau, Power BI

Experience

Senior Data Scientist Acme Corporation Anytown, CA 2020 – Present

  • Developed and deployed machine learning models to predict customer churn, improve product recommendations, and optimize marketing campaigns.
  • Led a team of data scientists in developing a new data pipeline to support real-time analytics.
  • Worked closely with business stakeholders to identify and solve data-driven problems.

Data Scientist XYZ Company Anytown, CA 2018 – 2020

  • Developed and implemented machine learning models to improve the efficiency of manufacturing processes.
  • Built and maintained data pipelines to support data analysis and reporting.
  • Presented findings and recommendations to stakeholders at all levels.

Education

  • Master of Science in Data Science, Stanford University
  • Bachelor of Science in Computer Science, University of California, Berkeley

Awards and Honors

  • ACM SIGKDD Data Mining Cup, 2nd Place (2023)
  • Google Anita Borg Memorial Scholarship (2018)

Projects

  • Developed a machine learning model to predict customer churn with an accuracy of 95%. This model saved the company millions of dollars by identifying customers who were at risk of leaving and taking steps to retain them.
  • Built a real-time analytics platform to track and monitor website traffic. This platform enabled the company to identify and respond to traffic spikes and outages quickly and effectively.
  • Developed a machine learning model to recommend products to customers based on their purchase history and browsing behavior. This model increased the company’s conversion rate by 10%.

References

Available upon request.

Machine Learning Experience

  • Developed and deployed machine learning models to solve a variety of business problems, including customer churn prediction, product recommendation, marketing campaign optimization, and manufacturing process optimization.
  • Used a variety of machine learning algorithms, including supervised learning (linear regression, logistic regression, decision trees, random forests, gradient boosting machines), unsupervised learning (clustering, PCA), natural language processing (NLP), and deep learning.
  • Worked with large datasets (millions of rows and hundreds of columns) using big data technologies such as Hadoop, Spark, and Hive.
  • Deployed machine learning models to production using cloud computing platforms such as AWS, Azure, and GCP.

Junior Data Scientist Template

John Smith 123 Main Street Anytown, CA 12345 (123) 456-7890 john.smith@email.com

Summary

Junior Data Scientist with a strong academic background in data science and machine learning. Eager to learn and apply my skills to solve real-world problems.

Skills

  • Programming Languages: Python, R
  • Machine Learning Algorithms: Supervised learning (linear regression, logistic regression, decision trees, random forests), unsupervised learning (clustering, PCA)
  • Data Visualization Tools: Tableau, Power BI

Education

  • Master of Science in Data Science, Stanford University
  • Bachelor of Science in Computer Science, University of California, Berkeley

Relevant Coursework

  • Introduction to Data Science
  • Machine Learning
  • Statistical Methods for Data Science
  • Data Mining
  • Data Visualization

Projects

  • Developed a machine learning model to predict customer churn for a retail company. The model achieved an accuracy of 85%.
  • Built a data pipeline to collect and process data from social media platforms. The pipeline was used to analyze customer sentiment and identify trends.
  • Developed a data visualization dashboard to track key performance indicators for a marketing campaign. The dashboard provided insights into the effectiveness of the campaign and helped to identify areas for improvement.

Awards and Honors

  • ACM SIGKDD Data Mining Cup, Honorable Mention (2023)
  • Google Anita Borg Memorial Scholarship (2022)

References

Available upon request.

Data Science Manager Template

Jane Doe 123 Main Street Anytown, CA 12345 (123) 456-7890 jane.doe@email.com

Summary

Data Science Manager with 7+ years of experience in leading and mentoring teams of data scientists to develop and implement machine learning solutions to solve complex business problems. Proven ability to manage projects, communicate effectively with stakeholders, and deliver high-quality results on time and within budget.

Skills

  • Data Science: Machine learning, statistical analysis, data visualization, data mining
  • Project Management: Agile methodologies, project planning, resource management, risk management
  • Team Leadership: Mentoring, coaching, performance management
  • Communication: Technical and non-technical presentations, written reports, stakeholder engagement

Experience

Data Science Manager Acme Corporation Anytown, CA 2020 – Present

  • Led a team of data scientists in developing and implementing machine learning models to improve customer retention, increase product sales, and optimize marketing campaigns.
  • Managed multiple projects simultaneously, ensuring that all deadlines and budgets were met.
  • Worked closely with business stakeholders to identify and solve data-driven problems.
  • Mentored and coached data scientists, helping them to develop their skills and knowledge.

Data Scientist XYZ Company Anytown, CA 2018 – 2020

  • Developed and implemented machine learning models to improve the efficiency of manufacturing processes.
  • Built and maintained data pipelines to support data analysis and reporting.
  • Presented findings and recommendations to stakeholders at all levels.

Education

  • Master of Science in Data Science, Stanford University
  • Bachelor of Science in Computer Science, University of California, Berkeley

Awards and Honors

  • ACM SIGKDD Data Mining Cup, 2nd Place (2023)
  • Google Anita Borg Memorial Scholarship (2018)

References

Available upon request.

Data Science Management Experience

  • Led and mentored teams of data scientists in developing and implementing machine learning solutions to solve complex business problems.
  • Used data visualization tools to present to management.
  • Managed multiple projects simultaneously, ensuring that all deadlines and budgets were met.
  • Worked closely with business stakeholders to identify and solve data-driven problems.
  • Mentored and coached data scientists, helping them to develop their skills and knowledge.

Note that these data scientist resumes are just examples. Don’t simply copy resume examples off the Internet – tailor each resume according to the data science job you are applying for.

What Should You Include in Your Resume?

In essence, your data science resume should effectively demonstrate why you are the perfect candidate for the role you’re applying to. So, the key element of deciding what to include in your resume is relevance: Include everything that is relevant to the role. You should also plan to write a data scientist resume summary that summarizes all of the data science projects you’ve worked with, the data analysis tools you’ve used, and any job experience you have in the data science industry. In this section, we’ll discuss everything you can include in your data scientist resume, whether you are are applying for entry-level data science jobs or prepping your data science manager resume. An effective data scientist resume is your key to that data science manager role! Even better – a great data science resume + a data science portfolio!

Name and Contact Information

Once the recruiter has seen your data scientist resume and you’re shortlisted, they would want to contact you. To make this seamless, include your contact information clearly and prominently. But remember that this is simply functional information. So, keep it concise. Double-check that it’s accurate. 

Include:

  • Name
  • Email ID
  • Phone number
  • LinkedIn, portfolio, or GitHub profiles, if any

Career Objective/Summary

This is often the first section in any resume. As a fresh graduate, without much professional experience, the career objective section acts as an indicator of what you would like to accomplish at the job you’re applying to. On the other hand, if you have some experience, it is better to include a personal profile, summarizing your skills and experiences.

A few things to keep in mind while writing your career objective/summary:

  • Use this section to narrate your professional story, so paragraphs with complete sentences work better than a bulleted list
  • Mention the years of experience you have
  • Provide information on the industry, function, and roles you have worked in

While creating your resume, it is sometimes better to write this section last. Making the rest of your data scientist resume will help hone in on the right summary. Also, remember to customize your summary while applying for the job. Not all data scientist jobs are the same, so your summary should reflect what you can do for the particular role you’re applying to. You should also include any data science projects you may have worked on during your studies.

Work Experience

As a practical field, work experience is more important in data science jobs than theoretical knowledge. Therefore, this is the most crucial part of your resume.

If you are a fresh graduate, make sure to include any internships, personal projects, open-source contributions you might have.

If you’re an experienced data scientist, spend enough time to tell your professional story clearly: 

  • List your work experience in reverse chronological order, with the most recent work listed on top and the others following
  • Indicate your designation, name of the company, and work period
  • Write 1-2 lines about what you were responsible for
  • Include the tasks you performed on a regular basis
  • Demonstrate outcomes—if you have produced quantifiable results, be sure to include them. For instance: “I built a production prediction engine in Python page that helped reduce crude oil profit loss by 22%”
  • Add accomplishments like awards and recognitions, if any

Layout-wise, follow consistency within this section. For instance, if you use bullets to list your tasks, use them uniformly across all your job titles. 

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Projects

Showing your hiring manager a peek into the work you’ve done is a great way to demonstrate your capabilities. The projects section can be used for that. While deciding which of your projects to include in your resume, consider the following:

Relevance. You might have worked on several projects, but the most valuable are the ones that are relevant to the role that you’re applying to. So, pick the most relevant 2-3 projects you’ve worked on. 

Write a summary. Write 1-2 lines about the business context and your work. It helps to show that you know how to use technical skills to achieve business outcomes.

Show technical expertise. Also include a short list of the tools, technologies, and processes you used to complete the project.

It is also an option to write a detailed case study of your projects on a blog or Medium and link it here.

Skills

The first person to see your resume is often a recruiter who might not have the technical skills to evaluate. So, they typically try to match every resume to the job description to identify if the candidate has the skills necessary. Some organizations also use an applicant tracking system (ATS) to automate the screening. Therefore, it is important that your resume list the skills the job description demands. 

  • Keep it short
  • Include all the skills you have that the job description demands
  • Even if you have mentioned it in the experience or summary section, repeat it here

Education  

Several data scientist jobs today need you to have a bachelor’s degree in computer science, statistics, or related fields. However, this is only a checklist item. Given the supply gap, we discussed earlier, hiring managers are willing to consider candidates without a formal degree but hands-on experience.

So, keep this section concise and clear.

  • List post-secondary degrees in your education section (i.e., community college, college, and graduate degrees)
  • Include the year of graduation
  • If you’re a fresh graduate, you can mention subjects you’ve studied that are relevant to the job you’re applying to 
  • If you have a data science certification or have completed a related online course, make sure to include them as well

Awards or Recognitions

In addition to the recognition you’ve had at your workplace, if you have other accomplishments, include them here. This might be Kaggle competition results, Github awards, etc.

How to Make Your Data Science Resume Stand Out

Do Your Research

Employers care less about you wanting a career in data science than they do about you wanting a career as a data scientist with them. “A tailored resume separates applicants who just want any job from those who want this job,” said Jon Brodsky, country manager for Finder.com. Before you start hacking together a data science resume, make sure you know to who you’re sending the resume.

Realistically, your resume won’t be wildly different for each application you file, but it should be somewhat different. The importance of this process is usually encapsulated in one word: fit. Are you a good fit for the company? Does your data science resume reflect the fact that you’re a good fit?

Read and re-read the description. The description is the most important piece of information to keep in mind. Your resume should demonstrate that you fill the job description: in experience, skills, location, etc. Include keywords in your data scientist resume summary, per the examples above.

Read the “About” page. So you found a position at a company, and you know nothing about it? The best place to start is the “About” page or the page that gives an overview of the company, its mission, its values, etc.

Check out the company blog. If the company has a blog, read through it. This will give you a lot of detail about what it’s trying to do, who its target market is, the company voice, and much more.

Browse product pages and other site pages. Find out what the company is selling or doing. Make sure you’ve gotten a good birds-eye-view of the company, keeping in mind how you can help increase revenue from a data scientist’s point of view.

Scour the internet. Expand your search. Good external resources for learning what you might want to know include glassdoor.com, LinkedIn, and various media outlets that might have published articles and press releases related to the company.

Tip: This doesn’t have to take more than 30 minutes.

Choose a Good Template and Design

A well-designed resume is sure to catch the eye of a recruiter among the numerous ones they go through. But remember that just because a so-called “design resume” looks good doesn’t mean it is good or even effective.

You can select a pre-existing template, design your own, hire a professional graphic designer, and much more. Nonetheless, some experts would say that you’re overthinking it. “The format of the resume itself doesn’t matter, but it should be brief one-page maximum,” Brodsky said. “Managers are busy and often inundated with data science resumes, so will only skim-read the first page anyway.”

Pick a resume design that employers can skim, not one they have to read. You don’t need fancy colors or logos on your resume. You don’t need a multimedia resume. You don’t need the best icons or visuals. However, if you use those, make sure they’re a reflection of you and not of a graphic designer you hired.

Organize Your Resume Layout

Organize Your Resume Layout

Given that recruiters decide on a candidate within seconds, it is crucial to provide them with all the necessary information at a glance. Here’s how you can do that.

  • Use headings and subheadings for clear differentiation of information
  • Highlight sections like ‘skills,’ ‘experience,’ ‘education’ etc., clearly
  • Arrange experience in reverse chronological resume format with the most recent on top
  • Give your relevant experiences take up the most space
  • Make sure there is enough white space, avoid your resume from looking cramped

Customize Your Resume

Circle back to your research: What makes you perfect for the role you are applying for? This is the question you should be answering in your resume. 

  • Mirror the resume—if you have skills that are mentioned in the job description, be sure to add it 
  • Write your resume in a way that’s suitable for the company culture. For instance, if you’re applying for a role at a self-driven startup, use language that shows you’re a good fit

What To Avoid

Since we just discussed the things to consider while crafting a resume, let’s also look at the things you need to avoid.

Too much information. Typically experienced professionals are tempted to include everything and its cousin in their resume. This can be counterproductive. Stick to the relevant information only.

Personal information. Your gender, race, marital status, sexual orientation, etc., is irrelevant to your job application. Do not include them. In fact, even photographs in resumes are illegal in several parts of the world.

Writing issues. Avoid spelling, grammar, and typography errors

Unprofessional language. While what’s professional has changed in the last few years, there are still boundaries. Don’t make your resume too colorful; stick to a neutral color palette. Use business language. 

Mistakes and exaggerations. Don’t overstate your qualifications or accomplishments. Check that your contact information is up-to-date. Ensure all the links you’re providing work.

Where Is the Best Place To Find a Data Scientist Resume Example?

Building a standout resume in the competitive field of data science requires careful consideration. You need to effectively communicate your skills, experience, and achievements to impress recruiters and land that coveted interview. But where do you begin? Look no further than the power of data scientist resume examples.

Data scientist resume examples offer invaluable templates and insights. They showcase diverse approaches to resume writing, allowing you to tailor your own to perfectly match your career stage and target job positions. Analyze different data scientist resume examples to see how experienced professionals present their accomplishments in data analysis, machine learning, programming languages, and project management.

Remember, while data scientist resume examples provide guidance, they shouldn’t be verbatim copied. Inject your unique personality and tailor your content to the specific job requirements. Highlight impactful projects using quantifiable results to demonstrate your true potential. Don’t forget to proofread meticulously – spelling and grammar errors can be dealbreakers.

Here are a few top sites for finding real-world data scientist resumes or theoretical data scientist resume templates: 

  • Beamjobs.com: Here you’ll find a data scientist resume example and cover letter to help you create a complete job application. 
  • Resumeworded.com: This site is packed with examples and you’ll definitely find a data scientist resume example to get you started. 
  • Enhancv.com: Here you’ll find a data scientist resume example to inspire your own, as well as an example of an entry-level analyst, tech, SQL developer and Tableau developer. 
  • Indeed.com: Indeed has a data scientist resume example as well as data scientist resume template you can use. 
  • ResumeBuilder.com: Here you’ll find a data scientist resume example for 2023 and beyond, focusing on AI, machine learning and more. 
  • DataQuest.io: The website has a great data scientist resume example as well as an eight-step guide for creating your own. 
  • Novoresume.com: This website offers a data scientist resume example and a guide for 2023 and beyond. It provides a data scientist resume example created with their online resume builder, along with additional resume examples for professionals in the computer science field, such as data analysts, data entry professionals, and artificial intelligence engineers.

By effectively utilizing data scientist resume examples as inspiration, combined with your own skills and dedication, you can craft a resume that truly shines and opens doors to exciting data science opportunities.

Data Science Resume FAQs

Are Resume and CV the Same?

While a resume is short, consisting of only 1-2 pages, summarizing your professional experiences, a CV is a longer document that details the whole course of your career. However, today, most recruiters and hiring managers use them interchangeably. Unless the organization specifically asks for a detailed record of your career, you can safely assume that they mean a resume.

Should You Customize Your Data Science CV for Each Job?

Yes, you should. When you send a generic resume, you burden the recruiter with the task of evaluating your suitability for the role. By customizing your resume, you clearly answer the question they have in mind. They will appreciate that. 

How Long Should a Data Science Resume Be?

Hiring managers are busy. They prefer to see a clear and concise resume. The standard practice is a single-page resume. But certainly, never go over two pages.

What Should I Include If I’m a Data Science Student?

As a data science student (or recent graduate), your resume should highlight relevant coursework, projects, and technical skills like programming languages, data analysis tools, and machine learning techniques. Include internships or work experiences, if any, and demonstrate your critical thinking skills, problem-solving abilities and passion for data-driven insights through practical examples or case studies.

What Skills Should You Put On Your Data Scientist Design Resume?

Your resume should have the technical skills that your job description demands. To make this process easier, first list all your data science skills and then match them with the role you are applying for, starting with your strongest skill. Data scientists must list their expertise on their resumes. Some commonly sought-after skills include data analysis, data wrangling, data mining, data visualization, statistical analysis, machine learning, predictive analytics, and programming. 

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.