How to Optimize Your Data Science Resume

This guest post was provided by Pathrise, an online mentorship program that works with students and professionals during each step of their job search. We have helped 700+ people land great jobs in tech through our workshops and 1-on-1 mentorships.

Recruiters look at hundreds of resumes per day and they often spend less than 10 seconds on each first glance. Therefore, it’s imperative that you spend time optimizing your resume to make it as strong as possible.

At Pathrise, we help data scientists find great jobs every day so we know what works (and what doesn’t). That is why we wanted to provide some of our data-backed tips to help you adapt your resume so that it tells your story and shows why you are the right candidate for the job.

1. Highlight the impact of your work

One of the biggest mistakes that we see is the way people write the statements on their resumes. If you just list the work you did without explaining why you did it and why it was important, odds are the recruiter will not care. We call these “grunt statements” because they only describe the grunt work that you did in the role.

Here is an example of a grunt statement:

• Worked on X for Y
• Cleaned datasets for visualization

So, how do you make your statements more interesting and thus, strengthen your resume? Transform your grunt statements into impact statements. These focus on the reasoning behind your actions and tell what you actually accomplished. Adding context to your resume entices recruiters to keep reading and provides them with valuable background information so that they can fully understand why your work was important.

Review these examples of impact statements:

• Worked on X to accomplish Y, resulting in Z
• Built a prediction model to reduce potential fraud by ~20% from the previous year, preventing $2M+ in potential fraud losses.

2. Quantify your accomplishments

As you can see from the updated impact statements, numbers also play a key role. Adding quantification as much as possible will help you tell the full story. Plus, when a recruiter is skimming your resume, the numbers will jump out to them.

It might be hard to find numbers for all of your statements, so start by asking yourself some questions about each one.

1. What was the scale?
   a. How large was your dataset or many rows of data did you analyze?
   b. How many different methodologies did you implement?
   c. Did you manage people or teams? If so, how many?
   d. What and how many scenarios/permutations/tests did you consider/handle?
2. What results were achieved?
   a. How many users/groups used it?
   b. How much money did you produce or save for the organization?
   c. How many many hours did you save the company?
   d. What percentage of the old process did you replace?
   e. By what percentage did you improve the old process?

3. Make your resume skim-friendly

You need to make sure that your resume is easy to visually digest since you do not have a lot of time to catch the recruiter’s attention. In addition, since recruiters look at so many resumes, they become sticklers for certain elements. Consistent spacing is one of these sticking points, so make sure you are very careful in this regard. Do not use more than 2 columns and keep it clean and professional. We also recommend that you use a sans serif font because these are typically considered more modern. This helps give the impression that you are tech-savvy.

Adding color for emphasis on certain points is fine as long as you do so sparingly and only use one. We suggest cool colors like blue or purple rather than warm ones like red, which can be alarming. Do not use any non-standard bullet point styles either because applicant-tracking systems often cannot understand them.

You should definitely include a designated section that shows all of the tools that you use and your skills, but do not use stars or numbers to indicate your proficiency for each one. If you put something on your resume, you should be comfortable with it, so there is no need to add arbitrary ratings. Try to make the language you use match up with the language in the job description. For example, if they are looking for someone who knows “Excel” and you have “Microsoft Suite” on your resume, update it to include the word “Excel.”

These tips are just the beginning. We have an annotated, editable data science resume template that you can use to optimize and enhance your resume. Then, you can begin sending cold emails to hiring managers and recruiters with the confidence that you are sending an extremely strong resume. Our fellows have seen their application responses triple after updating their resumes using these tips and sending out compelling cold emails. You can also check out our guide on how to get a data science job.

Learn more about the data science bootcamp and other courses by visiting Practicum and signing up for your free introductory class.

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