Updated in 2023
Hi, I’m Patrick, I write about the job search. After graduating from Cornell, I became a content lead at UBS where I helped professionals at Fortune 500 companies understand their stock options, salary, and benefits. When I’m not writing about the hiring process, I write novels for teens.
Research done by the Occupational Outlook Handbook reports that the job outlook for data scientists is increasing at a rate of 36%. Data science roles have been increasing in number year after year for many years.
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 2000+ 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 at a first glance it won’t get a second. Once your resume catches their eye, you need the best content to move you forward.
We want to share to of the biggest resume mistakes we see in our fellows and 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 internal word for resume points that only show what you were assigned to do and what you did in the role. You’re essentially describing the grunt work, but that is usually not a good description of how you spent your time in any past experience or project. Grunt statements usually look something like ‘Developed X for Y’ or ‘Worked on X using Y.’ Grunt statements are sometimes a necessary evil, but for the most part, they should be avoided.
Instead, optimize your resume by making use of…
Impact statements. These are statements that focus on your accomplishments and results. Highlight your impact on projects. They usually follow a structure that’s more like ‘Accomplished X by implementing Y, which led to Z’ or ‘Developed X to accomplish Y, resulting in Z.’ This takes up a bit more space in your resume but pays serious dividends. Analyze job descriptions when writing these statements to mirror some language of the values and goals for added impact.
- For example, a resume for a data science position should add much more technical language and deep explanation than a resume that’s focused on customer relations.
Mistake 2 – Lack of quantification
We know it might be hard to find numbers to quantify projects that haven’t been launched or weren’t that successful. However, if you ask the right questions, you can find the right information. Here are some questions that should help you quantify your work.
What was the scale?
- How many devices did I serve?
- What was the number of scenarios/permutations/tests that I considered/handled?
- How large was my dataset or many rows of data did I analyze?
- Did I implement different methodologies and if so, how many??
- How many people did I manage or teams did I act as a liaison for?
What did I achieve as a result?
- How many users did I launch to or will I launch to?
- What did I produce in value?
- By what percentage did I improve our old process?
- Who used it? How many users/groups?
- How many many hours did I save the company?
- What percentage of our old process did I replace?
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 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.