Hi, I’m Olga! I have years of experience in data science, most recently at eBay and now I work as an industry mentor at Pathrise, helping data scientists land a great role through technical workshops and 1-on-1s. Check out my article with tips for you data science resume.
Updated in 2021
Research done by the American Statistical Association reports that “nearly nearly 70% of business leaders in the United States will prefer job applicants with data skills by 2021.” Data science roles have been increasing in number year after year as well, skyrocketing 256% from December 2013 to December 2020.
That means that more recruiters are reviewing data science resumes than ever before and it is more important than ever to stand out, both in formatting and content. At Pathrise, we have helped 800+ people land their dream job, so we know what works and what doesn’t. Unfortunately, we see so many people make use of the same Microsoft or Google resume templates. If you can’t stand out from a first glance, your resume runs the risk of being put aside in favor of those that do spark interest. Once your resume catches their eye, you also need the best possible content to move you forward.
I have looked over thousands of resumes, as both an interviewer and mentor, and so I wanted to to share the 2 biggest mistakes I see. I also waned to include the most important tips for a successful data science resume. Knowledge of these issues and tips will help your resume make it through the first review and move you from application to interview.
2 biggest mistakes on data science resumes and how to avoid them:
Mistake 1 – Grunt vs. impact
Grunt is our team’s word for resume points that are focused only on what you worked on because you were assigned to do it (essentially grunt work). You might think that you need to talk about these tasks, and sometimes you do, but generally this is not a good description of how you spent your time in any past experience or project. Grunt statements usually look something like ‘Analyzed X for Y’ or ‘Worked on X using Y’.
Instead, we recommend optimizing your resume by making use of…
Impact statements. These are statements that focus on what you accomplished while doing the work that you did. So, you can still include your tasks, but within a structure that highlights your results. Some examples are: ‘Accomplished X by implementing Y which led to Z’ or ‘Developed X to accomplish Y, resulting in Z’. These sentences might be a little bit longer, but being able to show the recruiter that your work mattered is definitely worth it.
Mistake 2 – Lack of quantification
One of the biggest issues we see on resumes is missing quantification. We know it might feel hard to find numbers for projects that haven’t been launched yet or didn’t achieve the desired results. However, if you ask the right questions, you can usually find information to help you quantify either the scale or the results. Here are some questions that should help you find this data.
What was the scale?
- How large was my dataset or many rows of data did I analyze?
- What and how many scenarios/permutations/tests did I consider/handle?
- How many devices did I serve?
- Did I manage people or teams? If so, how many?
- How many different methodologies did I implement?
What did I achieve as a result?
- How many many hours did I save the company?
- How many users/groups used it?
- What percentage of our old process did I replace?
- How much money did I produce in value?
- By what percentage did I improve our old process?
Top 5 tips to make your data science resume stand out
Keep your font modern
We always recommend sans serif fonts (fonts without the feet) for data science resumes because they are look more modern. When you use these, you will let recruiters know that you are tech-forward, which gives the right first impression.
Don’t be afraid of colors
You can use colors to make important parts of your resume stand out quickly. But, make sure you stick to cool colors like blues, greens, purples, and teal rather than warm colors like red. Red sets off a “fear alarm” for people, so it is best to be avoided. And only use one color so your resume looks professional.
Readability is royalty
Your resume needs to be readable. That is the most important aspect of formatting. Make sure you have a maximum of 2 columns and never use white or light-colored text. If you include links to your portfolio, GitHub, and/or additional websites you’ve worked on (and you should!) ensure that they are clickable so that your work can be seen.
Emphasize important keywords
The recruiter and hiring managers do not spend a long time looking at your resume. In fact, it is usually less than a minute on first glance. Therefore, you should make their jobs easier by showcasing the keywords that matter most to them. If you are applying for a position that requires certain languages or skills, ensure that they are easy to pick out. If you can, tailor your resume for each specific job.
Always provide context
A common mistake on most resumes is a lack of context. Make sure to include the important key words, but also tell the story and purpose of the product, app, or system you worked on. This helps the person understand your impact.
If you are ready to start on your job search, check out our guide to landing a great data science job.
Pathrise is a career accelerator that works with students and professionals 1-on-1 so they can land their dream job in tech. With these tips and guidance, fellows have tripled their application response rate..
If you want to work with any of our mentors 1-on-1 to optimize your data science resume or with any other aspect of the job search, become a Pathrise fellow.
Apply today.
