How to Write the Perfect Data Science Resume in 2020

Writing a clear and concise data science resume will help you in your pursuit to land that perfect data science job at that next new startup or growing company.

While most human resource departments will mainly rely on the technical team or development team within the company to make the final hire for the data science role;

You will need a good data science resume to get into the front door.

Step 1: Choose a good template

Choosing a good template can be a great way to standardize your resume’s look and feel.

Remember, human resource managers will look at thousands of data science resumes each day, and may not spend more than 10 , 15 , or 30 seconds looking at your resume.

With this being the case, you’ll want to make sure that your data science resume is easy to read and will also stand out amongst the other data scientists.

By having a good template you can make it easier on the hiring manager to scan through your resume.

There are lots of great places to find resume templates, check out Indeed, ResumeGenius, or Monster for great resume examples for data science or machine learning roles.

Step 2: Organization and Prioritizing is the key

Be sure to include the following information:

Name: Name, Address, Email

Education: School, Degree, Dates, Certificate, Issuing Body, Bootcamps or Continuing Education Certificates, Valid Dates

Experience: Position, Company, Address, Dates

Responsibilities for each position

Skills and Knowledge: Languages, Programming Skills, Technical Skills, Soft Skills

Projects: Projects with links to your Github account, Kaggle account, or other sites where you can show off your skills

Step 3: Include important projects

For technical roles, the most important thing is if the candidate can actually do the job.

It’s a little different for other non-technical roles where soft skills can be more important.

For data science roles, the most critical area that the hiring team will be asking themselves will be:

a) Does this person actually have the abilities to do the job? Yes/No?

b) Where are his/her projects on Github / Google Colab / Kaggle etc or other sites where I can have my senior data scientists review their work and check to see if it’s good or not?

(Check out Smart Data Collective to learn more about why Kaggle is awesome for Data Scientists)

c) Will I need to hold this person’s hand and babysit them through the work day or will they be able to handle their data science workload independently?

These questions are important for you as a data science candidate because these are the questions the hiring people will be asking themselves when reviewing your resume and you work.

So show them your work, the more the better, so they can see that you’re not a rookie and you know what you’re doing.

Make sure your data science resume is clean and mistake-free

When you’ve finished compiling all of the relevant information for you data science resume, you’re going to want to make sure that it’s error free.

As mentioned in countless other articles about writing the perfect data science resume, you cannot have any errors, spelling mistakes, or inconsistencies with your resume.

Hiring manager are looking for any reason to cross you off the list of potential candidates, so don’t give them that opportunity.

Have at least three (3) people to audit your data science resume

We all know you are awesome, there’s no doubt about that, but you’re still human, and humans make mistakes — it’s part of the game.

So in order to reduce your chances of being disqualified for this amazing data science role, have at least three people read over your resume.

Check for readability, mistakes, or restructuring anything that might not make sense.

Don’t be embarrassed about sharing your data science resume with other people, that’s what it’s for.

Once you’re done with that, you should be good to go with your resume!

Congrats! You’re now ready to start chatting with potential employers.

Bonus Section: Questions they might ask once you get the interview

Once you’ve gotten your superb data science resume into the door of the hiring company you want to work for, the next step is going to be the technical interview.

Again, you’re being interviewed for a technical position, so you better have your answers ready for the technical team that’s going to interview you.

A great resource can be found here.

This is a pretty exhaustive list of potential data science or machine learning questions that the technical team might ask you.

This list of questions was written in 2018, so there may be some new questions they might ask with regards to new technology that’s been released.

Be sure to do you homework on newest technology being used in the data science field.

Bonus Section #2: Check out Kaggle’s great video on How to build a Data Science Resume

Kaggle did a great job of putting together a nice video where break down in even more detail all of the things you should be putting into your data science resume and portfolio. I’ve also included it below.

And with that, I wish you luck! Go out and get that data science job!

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