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.
According to the January 2019 report from Indeed, data scientist job postings jumped a full 31% in December 2018, compared with the same period the year before. Since December 2013, postings have skyrocketed 256%.
That means that more recruiters are reviewing data science resumes than ever before and it’s more important than ever to stand out, both in formatting and content. 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.
Our advisors have looked over thousands of resumes, as both interviewers and advisors, and so we asked them to share the 2 biggest mistakes they see and the most important tips for data science resumes. 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 were assigned to do and what you worked on. You’re essentially describing the grunt work that you did. You might think that you need these, and sometimes they are a necessary evil, but generally this 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’.
Instead, optimize your resume by making use of…
Impact statements. These are statements that focus on what you accomplished and what your results were. So, you can still include the information from above, but within a structure that’s more like ‘Accomplished X by implementing Y which led to Z’ or ‘Developed X to accomplish Y, resulting in Z’. They might sometimes 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. But, if you ask the right questions, you can find information to help you quantify. 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?
- How many scenarios/permutations/tests did I consider/handle?
- How many devices did I serve?
- How many people did I manage or how many teams did I act as a liaison for?
- 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 more modern-looking. When you use these, recruiters will see that you are tech-savvy and that you know how to give 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, it’s important to use cool colors like blues, greens, purples, and teal rather than warm colors like red. Red sets off a “fear alarm” for people, so it can feel too aggressive. Be sure to stick with one color so your resume looks professional.
- Readability is king
Your resume needs to be readable and that is the most important aspect of formatting. Make sure you have a maximum of 2 columns and you 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 are not spending a long time looking at your resume. Often, your resume has less than a minute in front of them. 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 so that the person who is reviewing your resume understands your impact.
If you are ready to start on your job search, check out our guide to landing a great job in data science.
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, we’ve seen triple the responses to applications and interview scores that double.
If you want to work with any of our advisors 1-on-1 to optimize your data science resume or with any other aspect of the job search, become a Pathrise fellow.