Since our beginning, we've reviewed countless data scientist resumes, and have made a concerted effort to distill what works and what doesn't about each of them.
The number one tip to create an effective data science resume is to quantify your impact on the business! As a data scientist, you need to prove you know how to focus on the metrics that matter to a company.
The nine data scientist resume samples below and our data scientist cover letter templates can help you build a great job application in 2023, no matter your career stage.
Whether you're looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you'll find plenty of tools to build your perfect resume, like our new Word resume examples or free Google Docs resume templates.
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Why this resume works
Why this resume works
Why this resume works
Why this resume works
Why this resume works
Why this resume works
Why this resume works
Why this resume works
Why this resume works
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 2023 takes you section by section through your resume to ensure you get that first interview.
You can successfully choose a winning resume format in 2023 that will snag an employer's attention.
Let's jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you'll get an interview.
When talking about your previous work (whether that's for another employer or on a side project), your goal is to convince the person reviewing your resume that you'll provide value to their company. This is not the place to be humble. We want to see that "I'm wearing my favorite outfit" level of confidence.
The template for successfully talking about your experience as a data scientist is:
You're a data scientist, so highlight your value by demonstrating the quantitative impact of your work. These can be estimates. For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:
Numbers draw attention, are convincing, and make your resume 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 resume in numbers.
Simply put, the more work experience you have, the less space "projects" should take up as a section on your resume. In the sample resumes above, you'll notice that only the more entry-level data scientist resumes have a section for projects.
The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you'll want to focus on the bullets 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.
Junior data scientists should include projects on their resumes. Try starting with a resume outline, where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. 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 get 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 pulled together some data, laid out his assumptions and methodology, and made his conclusions.
No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer 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.
Since you have limited space on your resume, you should only include a resume objective if you take the time to customize it for each role to which you apply.
You may want to include a resume summary or objective when you're making a big 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 personalize it to each job.
Include the title of the job you're looking for under your name. This should be aspirational. So if you're a data analyst looking to apply for data scientist jobs, you would put "data scientist" under your name as the headline:
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 takes too much space. This is a big red flag for hiring managers.
The reason people make such an exhaustive skills section is to get through the mythical data science resume 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 resume is this: if it's on your resume, 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 resume. If you only have a handful of tools under your toolbelt, 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 kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that's significantly lower than the actual number.
There are tons of fish in the job market sea; you just need a fishing rod.
Generally, the more senior you are, the shorter your skills section needs to be. If you're a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the "Work Experience" section. Show how you used particular models in the context of your work.
When you're more junior, you likely haven't had the chance to use all of the techniques you're comfortable with within work or a project. That's okay! It's expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.
Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you're looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.
Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you're looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it's not necessary to include it.
If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume's "Projects" section.
The takeaway from this section is simple: this is not where you should make a mistake. Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled 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 math. We've seen exactly four data science resumes where the email address on the resume was incorrect.
Make sure your email address is appropriate. While we don't doubt the authenticity of your "officefan4life@gmail.com" email, maybe don't use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.
This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.
This section is just a list of one-off styling and formatting tips for your data science resume:
You don't have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:
Let's walk through a specific example to highlight what we mean by including particular projects for different jobs. 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 had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.
So all we'd do is change that section of his experience at EdTech Company to talk about that project, as you see below:
Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue
Customized for the role: Built out our company's ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring
For early-stage startups (anything less 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 instruction.
If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like "drove" or "built" instead of passive language like "worked on" or "collaborated on." We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.
There you have it—a compelling, easy-to-read data science resume built for 2023. 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 resume, you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don't let that be the reason you don't get an interview.
Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.
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