How to Write a Winning Data Science Resume | Towards Data Science

IntroductionAestheticGoalExperiencesSkillsEducation/ExtracurricularsSummaryReferences
Introduction
Aesthetic
Goal
Experiences
Skills
Education/Extracurriculars
Summary
References
Attracting top tech companies can not only be extremely difficult but intimidating as well. However, I am here to tell you that you can indeed do it, you can catch the eye of “FAAMG” — that is Facebook, Amazon, Apple, Microsoft, and Google. This is not to say that your improved resume will not also captivate other tech companies and startups.
The theme of this article will not center around what you need to change about your future, but what you will need to do to market your past better. There are plenty of well-qualified data scientists out in the job market, and some may simply not apply to positions at these companies because they are overwhelmed, not believing in their work, and more importantly, themselves. With most things in life, confidence is key. Even more critical, are the skills that drive that confidence. However, in terms of your resume, you will need to articulate your skills in a way that is short, yet detailed. It is easy to assume employers will know what you have been working on when you refer to your past in general, but you will need to instead focus on the clarity of what you did at whatever company it was.
Below, I will give you five ways to improve your resume so you can appeal to top-tier tech companies.
Let us start with a non-technical improvement on your resume. While this point may seem negligible to your chances of attracting companies to your resume, it is actually quite the opposite. It could probably be said that most people click on an article or blog because of the main image, the title, or clicking on a YouTube video that has an enticing, yet mysterious thumbnail.
There are reasons why the visual representation of your work on a resume should not be ignored.
As cliche as it sounds, ‘do not fit in, stand out’. Imagine you are are a technical recruiter and you see the same bland, white and black ink resume a few hundred times per day. What is going to attract you first? It will be the aesthetic, the layout, and the uniqueness of your resume. Just like forming your own data science brand, you should focus on branding your resume. As an employer, you could think of hiring someone who has put work into a professional, yet creative resume, over someone else, as it will be their first impression of you.
Let’s look at mine.
As vulnerable and scary as this is to post my own resume online like this, I believe it will show you how you do not need to be a UX/UI designer to be creatively successful, but a little more creative than the next person is always nice — anyone can do this formatting, it just takes a bit of rearranging and organization (or you can use a premade template — but I prefer the homemade version — your homemade version). Okay, yes, this version of mine has been changed a little (specifically, the text of the most recent roles and their respective company names). However, the layout is the same as my original resume, as well as the heading, main section titles, dates, goal, and skills. Check it out below.
So what is so great about this resume? The easiest way to look at why — is that it stands out. It might not be the best or the most creative, but it is something I created that matches my personality. You want to land a job at a company that fits your personality. If a company gets upset that your resume is blue, then they might not be a match for you — or vice versa, and the opposite could be said. When I posted this resume on LinkedIn as well as applied to jobs on their sites, I felt nervous, vulnerable, and different. I asked a few friends what they thought about it before submitting and some loved it and some did not.
There is no perfect resume, but there is a better way to show your work.
I trusted my gut and wanted to show off my ingenuity. Just like my friends, some companies did not like my eclectic resume. However, some companies loved it, and pointed it out (most of those companies were the FAAMG companies), as well as other reputable big tech companies. To me, there was something to be said about that — these successful companies a lot of data scientists want to work at, admired creativity and personality — it paid off.
Controversially, I have two pages to my resume. I have heard the ‘one-page’ rule so many times, but have gotten maybe 1% of employers who reached out about the resume length. If you have got it, flaunt it. Show the employers the amount of incredibly hard work you have done to get to this point. If they are going to read one full page of your resume, they will most likely read 1.68 pages of your resume as well. If not, then they are not giving you a chance most likely anyway. If you believe two pages for your resume is needed, then do include two pages.
All in all, the aesthetic of your resume can be highly influential in the application process. The choice is, of course, ultimately up to you how you design your layout and look.
Here are the key tips on your resume’s aesthetic:
* find something unique about you and apply it to your resume* organize the layout in a way that lets the reader focus on each section separately* group your thoughts and work into sections: goal, experiences, skills, education, and extracurricular/awards. * give some breaks between sections so that the eyes can rest (just like how I made these points with a dark background)* tell a story with your layout: what do you want? what have you done? how did you get there?
I actually received some advice from a former Facebook employee that has stuck with me whenever I update my resume or let others know about what to put first on their resume. That section is the ‘Goal’ section. A good way to improve your resume is to include this goal section because it shows employers what you are looking for, almost answering the ‘what do you expect to do in five years’ question, interviewers often asked. It shows you are thinking ahead and have a plan. Additionally, it will let the recruiter and/or employer know if you are a match right away.
The goal section should include which role you are looking for, you can be specific here. So in mine, I put Data Scientist, but you could put ‘seeking a natural language processing role in data science’. Make sure to include why you are in data science, and what you want to do with it. You can also include some important links or usernames to common platforms used in data science. For example, I linked my GitHub and blog username. Surprisingly, a lot of employers pointed out that it was nice to see where they could access more information on me related to data science.
Here are the summaized key ways to improve on this goal section:
what role are you looking for?
what type of role do you want?
what will you do with that role in the business?
additional links or information on your data science knowledge
The meatiest part of your resume is the experience section. Of course, try to avoid paragraphs upon paragraphs. But, include longer sentences where necessary. If you are too short, it might not be that enticing of a project to someone who is viewing it for the first time — an extra few words will be fine.
I have always stuck to the same rule when writing out points in the experience section for each company on my resume. This rule includes three easy and simple main points. The first point is outlining the tools or programming languages you used. The second point is highlighting what you actually did. The third point, lastly, is articulating the results of what you did. So, altogether is, what did you use to complete this project, and what were its results? For example, ‘Utilizing Python in a Jupyter Notebook to create a Decision Tree classifier that helped to automate a manual classification process by 50%’. I truly think this method of explaining your experience in each major project is the best way to attract employers. It will show, in the quickest way possible, to the employers, possible coworkers, and stakeholders, your impact on a business problem. That is the foundation of data science; aiding the business by solving complex problems with unique tools.
The other information that you should make sure to include in your experience section are the company, title, and date. For example, ‘Data Scientist at Google from August 2018 to the current date’.
Here are the summarized key ways to improve your experience section:
what did you use?
what did you do?
what was the result?
Short and sweet. This section is a no-brainer. You will simply lay out all of the most common skills you have used for data science. Keep in mind that you may want to include simple skills like Microsoft Office, because, although it may seem implied to you, you should not assume recruiters already know this knowledge. Similarly, depending on the type of data scientist you want to be, there are certain skills that should be highlighted over others. For example, if you want to focus on business intelligence and product management data science, you will highlight SQL, Jira, and Confluence. On the contrary, if you want to appeal to more machine learning data science, highlight skills like GCP, Airflow, and Kubernetes. Additionally, if you want to stand out even a little more, make sure your skills look well-presented and have some pattern to them.
Here are the summarized key ways to improve your skills section:
skills applicable to the type of data science
include very specific
but also some very general skills
Like the skills section, the education section does not need to be long. Include your undergraduate degree and graduate degree university, along with their dates as main focuses like majors, minors, and concentration. If you are a beginning data scientist, you may want to include your GPA.
In addition to education, are the extracurriculars. This section is where you could also include your GitHub information or a similar project sharing site. You can also outline key projects that were outside of your professional, career data science experience.
If you do not have any formal, professional data science experience, then in the experience section outline those same outside data science projects instead.
Examples of these projects would be completing a tutorial, certificate, Bootcamp, or online course (like Kaggle data science courses).
Here are the summarized key ways to improve your education/extracurriculars section:
include undergraduate and graduate degree (if applicable)
keep it short and simple
highlight unique projects and certificates you have achieved
If you have reached it this far in the article, thank you. I want to give the most applicable and easy advice to data scientists so that they can find work at their dream company. I included improvements in the aesthetic, goal, experiences, skills, and education/extracurriculars sections of your resume. While I cannot guarantee landing a job, I do think you will improve your resume and hopefully, attract your dream job.
Once again, take my recommendations with a grain a salt. The goal of my article is to not only give you advice on these main sections of your resume, but to also give you inspiration and motivation on what new ideas you can come up with yourself. To summarize, you should focus on being yourself, highlighting the proudest accomplishments of your career (because you interviewers will pick the projects to interview you on, so know them well), and finding something unique and creative that can make your resume stand out.
I can attest that my resume screenshot is mine and has gotten attention from top-tier companies like Google, Apple, Facebook, and Microsoft. Keep in mind that there are other wonderful companies too. Just because a company is not one of these bigger tech companies, does not mean it is of any less value, and a different company could be rather more fun and beneficial as an experience. Ultimately, the next part of the process after appealing to companies is the interview. I have written several articles on data science and interview process and will link one now if you want to check it out — it refers to common similarities and differences amongst data science and data analytics interview processes[4]:
towardsdatascience.com
I hope you found my article interesting and useful. I wish you luck on your resume journey and data science career. Feel free to comment down below. Thank you for reading!
[1] Photo by Alex Haney on Unsplash, (2019)
[2] M.Przybyla, screenshot of resume, (2020)
[3] Photo by Paweł Czerwiński on Unsplash, (2018)
[4] M.Przybyla, Data Scientist vs Data Analyst Interview. Here’s the Difference., (2020)