20 Data Science Resume Tips: How to Write a Stand-out Resume
Job hunting this fall? It’s a great time to polish up your resume with these data science resume tips.
At Dataspace, our recruiters review hundreds of data science and engineering resumes every month. It’s tedious work, especially when we have to dig through a poorly-structured document. As a result, we’ve learned a thing or two about making quick sense of a resume.
In general, recruiters often spend as little as “30 seconds to look over someone’s resume and make a decision.” So, if you’re looking for work in the highly-competitive data science job market, there are several things you can do to increase your chances of making the cut. Of course, this presumes you actually meet the job requirements—that said, we regularly see highly-qualified data scientists and engineers make simple oversights and mistakes on their resumes that negatively affect how recruiters will read them.
Before we jump into specific tips, it’s important to think about your, and our, motivations here. Assuming that you’re honest and above board, your goal is to find a new contract or permanent job. You want it to be interesting, in an acceptable location, and at a fair compensation rate.
For us, and our clients, the following items are important:
- Is everything on your resume presented fairly and honestly?
- Do you have the skills and experience our client needs for the job at hand?
- Are you able to clearly communicate your skills, experience, and value?
While we’ve offered some general advice for data science resumes before, we’re going to break it down even more to some simple dos and don’ts.
Data Science Resume Dos
- Use your full name. When we receive a resume with only initials or without a last name, it raises questions. Especially if it doesn’t match the name on other parts of the application. Also note, an email address does not count as your name.
- Include contact information, and preferably more than just an email address. Again, this should match the contact info you supply in your application.
- Include your LinkedIn profile. Virtually every professional in the US has a LinkedIn account—you should too. Be sure it’s up to date with a reasonably professional photo. (A bonus insight here: if you don’t provide your LinkedIn profile, we’re going to look for it anyway and wonder why you didn’t supply it or why we couldn’t find one.)
- Include company name, location, and dates for every position held. When any of this info is missing, it raises questions. If you are concerned about a gap in your data science work experience, that’s what a cover letter is for. Make an effort to provide context for your situation. Gaps aren’t always a deal-breaker, and you’ll be better off being upfront about your experience than simply omitting information or misrepresenting assignment dates.
- Keep it simple. Five to seven bullet points per project should be enough to clearly sum up what you were working on, your responsibilities, and the tools you used. You don’t need to list every technology you’ve ever touched. To recruiters, more bullet points are not more impressive—it’s a red flag. Instead, focus on how the skills you used in past positions are relevant to the one you’re applying for today. An article from Dataquest states that “adding small details here and there in accordance to the job description would certainly impress the hiring manager/recruiter.”
- Make it about you. Clearly state what you did in a position, not what your team did. Recruiters are interested in your specific skills and abilities. Unless you’ve been employed by the company WeWork or WeR.ai, the word “we” generally doesn’t belong on your resume. (See the don’t below.)
- Tie your bullet points to the business problem you solved. Just listing that you’ve used a bunch of tools doesn’t help a recruiter understand what you can do with them—that’s the important part. Think of each bullet point as an answer to the question, how did your work help the company become a better business?
For example, a statement like, “Used Apache Kafka to stream data to AWS,” is pretty generic and could apply to most companies using Kafka and AWS. It doesn’t tell recruiters that you understand the value of your work. Compare that statement to this one, “Used Apache Kafka to stream claims data from on premise claims system to AWS S3 for loading into the data warehouse. This provided underwriters with access to near real time claims data.” Now the value of your work is clear.
You can find more great advice for writing effective bullet points (and more!) in Sharan Kumar Ravindran’s article on writing an impressive data science resume.
- Specify the tech stack for each position. It can be really helpful to recruiters if you include a brief tech stack section for each job. List the important elements of the tech stack you were working with for this project. This can ensure that the tools you used don’t get lost in the descriptive bullet points, and also helps your resume have keywords that the AI in an applicant tracking system (ATS) will be looking for.
- Keep it short. We understand that there’s a lot to fit in a resume for data science careers, but do your best to keep it to 3 pages or fewer. Just like bullet points, more pages are not more impressive. If you put the effort in to limit your experience bullet points, you shouldn’t have a problem with too many pages.
- Check for errors. Make sure your font and formatting is consistent throughout your resume. Double check your spelling, and make sure that autocorrect didn’t butcher the names of technology and tools you used. It may seem like a small thing, but errors definitely make recruiters question your attention to detail, especially when every writing app nowadays defaults to checking your spelling.
- Consider a cover letter. Your resume should tell the story of your career with you as the hero. If you feel tempted to embellish your experience to better suit a job description, don’t. You are way better off being honest about what you’ve done and using a cover letter to explain why it’s relevant to the position. Recruiters can see through lies. (See our don’ts list below.)
Data Science Resume Don’ts
- Don’t lie. Just don’t. Nothing should appear on a resume that isn’t 100% true. This includes your education, positions you’ve held, and the technologies you used. Seasoned recruiters can see through misrepresentations (and they also check references).
- Don’t waste space with company descriptions, and doubly so if it’s a well-known business. For example, don’t spend a paragraph noting that Citibank is a multinational bank with branches in 162 countries, etc. That just wastes space and really adds nothing of value for a recruiter. If the company’s services are relevant to your work, that will show up in well-written bullet points. (See make it about you above.)
- Don’t stuff your bullet points with keywords. Recruiters realize that you know their applicant tracking systems (ATSs) look for keywords in resumes to match those in the job description. So yes, you do want them on your resume, but just listing every technology that could possibly be relevant to data science is a transparent attempt to game the system. Recruiters even have a name for this: keyword loading. We offer a couple suggestions for handling this in our dos list above.
- Don’t use the words “like” and “we.” If you write that you used technologies “like Azure and AWS,” it’s not grammatically the same as saying you “used Azure and AWS.” Be clear about your experience. If you want to point out that something you used is similar to something required in the job description, note it in a cover letter. Similarly, it’s only mildly interesting to a recruiter if your team used a particular technology (i.e. “we used”) or did a particular task. “We” is doing a lot of work here, and recruiters know it. As noted earlier, stick to listing only what you personally did.
- Don’t repeat bullet points. It’s a red flag when the exact same bullet point appears under multiple positions on the same resume, and it’s something ATSs can easily detect. If you actually did the same thing in multiple jobs, make sure to tie each bullet point to the specifics for that business and project.
- Don’t list schooling as work experience. You should have a separate section for your education and training. Research projects and internships may be kept under work experience, but should be clearly labeled as such.
- Don’t copy and paste from other resumes and job descriptions you find on the web. Recruiters know to look for this and it’s pretty easy to figure out. And, as you can probably imagine this is a huge red flag.
- Don’t forget to check for errors. Yeah, we mentioned it in our dos list. But seriously, don’t skip this step.
Get noticed! A great resume will help you make the cut.
Yes, we get it—writing a resume is often an agonizing task. We hope these data science resume tips help make it a little less so. In a competitive job market, such as data science and data engineering, every little detail can help you stand out in a crowded pool of applicants. It’s definitely worth your effort as a job seeker to make your resume the best it can be.
Oh, and one last bonus data science resume tip:
- Be consistent! Once you have a stand out resume in hand and are ready to start filling out applications, be 100% sure that the answers you supply to questions on the job application or in the ATS match what’s in your resume. If you are applying for an analytics position and are asked how many years of experience you have with Power BI, and you reply with 5 years, but Power BI is nowhere to be found in your resume, that will raise questions with a screener.
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Does your resume check all the boxes? Apply to a data science, engineering, or analytics job today!