Since starting BeamJobs we’ve reviewed north of 1,000 data scientist resumes and I’ve 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 demonstrate you know how to focus on the metrics that matter to a company.
The five resume examples below, and the pro resume tips we give, can help you get started building a great data science resume in 2022 no matter what stage of your career you're at.
Whether you're looking for your first job as an entry-level data scientist or you're a veteran with 10+ years of experience, you'll find a resume to give you some inspiration. Further ideas are also available in our new Word resume examples (free).
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 7.4 seconds reviewing your resume so it’s vitally important that you catch their attention in that time. Our guide for 2022 takes you section by section through your resume to make sure you get that first interview.
You can successfully choose a winning resume format in 2022 that will snag employers' attention. If you’re short on time, here are the quick-hit summaries of each section that you can easily apply to your resume:
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.
Your goal when talking about your past work (whether that’s for another employer or on a side project) is to convince the person currently reviewing your resume that you’ll provide value to their company. This is not the place to be humble. I want to see that “I’m wearing my favorite outfit” level of confidence.
The template for successfully talking about your past experiences 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 talk about your projects in a quantitative way:
Numbers draw attention, they’re convincing, and they make your resume much more readable. Tell me, 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. You’ll notice in the three sample resumes above that only the entry level data scientist resume has a section for projects.
The senior level resumes focus on projects in the context of experience at 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 are a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.
Junior data scientists should absolutely include their projects on their resume. Try to share anything about your projects that is available. Can you share the Github link? Do you have a link to a write-up you did about your project?
For entry level data science projects the more initiative you can show the better. Do you have any questions you’ve always wanted the answer to? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, I wanted to know which data science job boards were best so I pulled together some data, laid out my assumptions and methodology, and made my conclusions.
No matter what projects you include in 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're going to take the time to customize it for each role you apply to.
The only time you may want to include a summary or objective is 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 if you include it, so take the time to personalize it to each job.
Instead of putting an objective or summary on your resume, make sure to include the title of the job you’re looking for under your name. This should be aspirational. So if I’m a data analyst looking to apply to data scientist jobs, I would put “data scientist” under my name as my headline:
The most common mistake I see on data science resumes (that I used to make on my resume) is what I call skill vomit. It’s a laundry list of skills that no one person could have expertise in. This is a big red flag for hiring managers. A quick rule of thumb: if the skills section is taking up a third of the page, it's taking up too much space.
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 mention R if you know both), you’ll have no problem with those keyword filters.
The rule of thumb that I 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 on 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 will be able to find jobs looking for that skill-set.
I can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows there are 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 a lot 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 specific 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 within the context of 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 that are relative to data science to demonstrate you have a strong foundation.
Classes in subjects like linear algebra, calculus, probability, statistics in addition to 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 really not necessary to include your GPA.
If you just finished (or are finishing) from a data science bootcamp this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to include a few projects from your bootcamp (especially if it was an original project) in the “Projects” section of your resume.
The takeaway from this section is simple: this is not where you should make a mistake. Storytime! When I was first applying to jobs out of college I realized about 20 applications in that I spelled my name “Stepen” instead of “Stephen.” Don’t pull a Stepen.
I’ve seen exactly four data science resumes where the email address on the resume was incorrect. Data suggests that when your email is wrong your response rate from companies drops to 0%. That’s just math.
Make sure your email address is appropriate. While I don’t doubt the authenticity of your “firstname.lastname@example.org” 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 analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything that is relevant to data and 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 I recommend to customize your resume for each job:
Let’s walk through a specific example to highlight what I mean for including specific projects for different jobs. Let’s say that the “Senior Data Scientist” from the resume at the top of the page is applying for the job below.
In the “Ideally you’d have" section they mention they want someone who has “Experience with ETL tools”. Well you’ll see on my resume that I have nothing about data engineering listed. In reality, I had a large role in building out data pipelines in my fictional role as a senior data scientist at EdTech Company.
So all I’d do is change that section of my 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 are 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 in your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on”. I 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 they can to weed people out.
There you have it. A compelling, easy to read data science resume built for 2022. 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 resume you took a huge step towards landing your next (or first) data science job. Now please, I beg you, check your grammar and spelling again and have someone else check 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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