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Five Essential Methods To Creating A Great Data Scientist Resume

How to write a resume that showcases your analytical skills and business acumen

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Photo by Franki Chamaki on Unsplash
Photo by Franki Chamaki on Unsplash

Data Scientist role is one of the highest-paying and most prestigious roles in 2021. As technology becomes the centre of our lives due to digital transformation in the last decade and data can be used to tell compelling stories to business executives, data scientists become an in-demand professional. There are both good and bad sides to this transformation. Let’s start with the good. If you are a data scientist, it means you will be sought-after and your chance of getting a job is high. You can be selective about the company you would like to work for, and you’ll be spoiled for choice.

So what’s bad? Well, this means that as a data scientist looking for a job, you will have more competition because other data scientists will be competing for the best company to work for, one that not only pays well but also will look good on their resumes. So the question then is how to have an edge above other candidates so that you get noticed by recruiters and hiring managers.

As a hiring manager in the tech industry and an interviewer for many tech jobs – not only for software developers and engineering managers but also for data analysts, product managers, UX designers, content writers, program managers, and technical recruiters – I’ve seen my fair share of resumes for tech jobs and what stands out. That’s why I’ve created a sample perfect resume template to help job seekers in the tech industry. Those with standout resumes perform better than average in interviews as well. This is because they have presented themselves in the best possible light and highlighted important information for interviewers to deep-dive into. As a result, their interviews are more focused and flow much smoother.


There are five essential methods to creating a great data scientist resume. Great data scientist resumes are effective, showcase your analytical skills as well as business acumen, land multiple interviews, and get you the job you want.

Without further ado, here are the five essential methods that you should apply when creating a data scientist resume:

  1. 🦄 Focus on your speciality.
  2. 🏆 Quantify your achievements.
  3. 🎯 Show the proof.
  4. 💡 Make it scannable.
  5. 🤖 Discard irrelevant information.

1. 🦄 Focus on Your Speciality

Photo by Myriam Jessier on Unsplash
Photo by Myriam Jessier on Unsplash

What’s one thing that you’re known for in the industry and data scientist community? It could be a specialised skill or a specific type of company or environment. For example, improving churn rate or working at fintech companies. Not sure what you’re known for? Look back at the last few years of your career, the projects that you’ve worked on, the companies that you’ve worked for, and identify some patterns. The thing with becoming better at anything is the amount of consistent practice, so it’s more than likely that your speciality is something that you’ve been doing repeatedly.

Once you’ve identified your speciality, highlight it in the short summary section of your resume. Moreover, call out that speciality of yours in the employment summary of each job that you have.


2. 🏆 Quantify Your Achievements

Photo by Lukas Blazek on Unsplash
Photo by Lukas Blazek on Unsplash

Compare these three examples of achievements:

  1. I worked on a pricing project with Company XYZ in my role as a lead data scientist.
  2. I worked on one of the biggest pricing projects with Company XYZ in my role as a lead data scientist. The project spanned over three months and I developed a sentiment analysis model.
  3. As a lead data scientist at Company XYZ, I worked on a pricing project for a marketplace site that receives 3 million unique visitors a month. The aim of the project was to increase an average spend of a customer, and as part of the project, I developed a sentiment analysis model that leveraged 200M+ existing records. The sentiment analysis model improved the average spend of a customer by 20% by the end of the financial year.

As you can see, the third example stands out because it not only explains the complexity of the project but also quantifies the impact that the candidate delivered in industry metrics. You can also tell that the candidate’s speciality is large data analysis by reading the example above.


3. 🎯 Show the Proof

Photo by Austin Distel on Unsplash
Photo by Austin Distel on Unsplash

How would a hiring manager or recruiter know that you’re really capable of what you say you can do or have done in your resume? By seeing the proof. Proof can come in one of three ways as part of your resume:

  1. Testimonials and 360° feedback from your past and present colleagues.
  2. Your work online (e.g. personal brand, your website, your portfolio).
  3. Awards you’ve received, as long as they are relevant to your career.

You can take a look at how to display such proof in the sample resume template that I’ve created.


4. 💡 Make It Scannable

Photo by Adeolu Eletu on Unsplash
Photo by Adeolu Eletu on Unsplash

It’s a widely known fact that recruiters and hiring managers spend less than ten seconds glancing at a resume. But that doesn’t mean there is no point to writing a solid resume with important information. This is because if there is a resume that catches their attention, recruiters and hiring managers will spend more time looking at it. They might not look at it immediately, but they will definitely come back to it. Therefore, the key is to make your resume easily scannable so that recruiters or hiring managers will make the decision within ten seconds that they want to spend more time reviewing your resume.

To make your resume scannable, use appropriate font size and colour for each heading, make certain keywords (e.g. your speciality) bold, group relevant info, list items in a bullet point, add white space to improve legibility, and last but not least, present information in order of importance from top to bottom. Those are some of the easy but effective ways.


5. 🤖 Discard Irrelevant Information

Photo by Mark Fletcher-Brown on Unsplash
Photo by Mark Fletcher-Brown on Unsplash

"Less is more." You may have heard this phrase, but have you really applied the theory in practice – especially in your resume?

When your resume is four or five pages long with too much information, it overwhelms hiring managers and recruiters. The risk then is diluting your important achievements with all the irrelevant and outdated information. Or worse, they may discard your resume altogether.

For example, if you made a career change a few years ago from a developer to a data scientist, you don’t need to list all your employment history as a software developer.

Likewise, if you’ve been working in the industry for more than a decade and are applying for leadership roles, it is OK to remove your first internship experience from your employment history.

"Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away." ― Antoine de Saint-Exupéry


Investing in Your Data Scientist Resume Will Pay Off

Photo by Glenn Carstens-Peters on Unsplash
Photo by Glenn Carstens-Peters on Unsplash

Having a good resume not only gets you interviews but also leads you to job offers. We all know this: First impression matters. Your first impression with your potential employer starts from the moment they see your resume – not when you meet them for the first interview. Moreover, the success of a data scientist lies in making data useful to influence real-world actions and business decisions and as a data scientist, there is no better way to apply this skill than in your own resume.

To summarise, here are the five essential methods to creating a great data scientist resume that will present you in the best possible light:

  1. 🦄 Focus on your speciality.
  2. 🏆 Quantify your achievements.
  3. 🎯 Show the proof.
  4. 💡 Make it scannable.
  5. 🤖 Discard irrelevant information.

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