Must-haves on Your Data Science Resume

Recruiters look at a resume for 7.4 seconds before making a decision on the candidate. So that means you have basically less than 10 seconds to make a good impression. 10 seconds is not a lot of time; especially when you really want this job.

Must-haves on Your Data Science Resume
João Ferrão via Unsplash


Writing a resume for any job is a task in itself, but when it comes to Data Science professionals this can be the make or break of landing the job you want. 

According to Ladders: Eye Tracking Study; Recruiters look at a resume for 7.4 seconds before making a decision on the candidate. So that means you have basically less than 10 seconds to make a good impression. 10 seconds is not a lot of time; especially when you really want this job. 

So what can you do to woo a technical recruiter with your resume? Here are some points you should consider.


Format and Structure


As a Data Scientist, you know how important format and structure are. 

The best format for your resume is reverse-chronological. This means your latest work, employment, etc is listed first. This helps recruiters see your current skills and what value you can add to them. 

The structure of your resume is very important. You don’t want your resume to look messy, and the recruiter gets frustrated and immediately closes it. There are many resume templates out there, you can simply search on Google and scan for a structure that you like. I have personally used Zety, and believe that it is a great tool for freshers.


What to Include in a Data Scientist Resume


The main elements a recruiter is looking for are:

  • Contact Information
  • Resume Summary/Objective
  • Work Experience
  • Skills
  • Education

The other aspects that make your Data Science resume top-notch

  • Projects
  • Awards & Certification
  • Interests & Hobbies

However, knowing what you write for each section and how is something you should consider. Let’s start off with Contact Information.


Contact Information


It might sound silly to have this as a section, but this determines if the recruiter can contact you or not, so it is very important. 

This section should include:

  • Full Name
  • Title - For example, “Data Scientist”
  • Contact Number - Make sure to check this for any errors
  • Email Address - A professional email is always the only option, for example,
  • Location - This gives the recruiter a better understanding of where you are based. You can also state if you’re willing to locate.


Resume Summary/Objective


As mentioned above, recruiters don’t spend a lot of time scanning through each resume. Some recruiters are looking for buzz words that fit their requirements such as ‘Python’, ‘Pipeline’, ‘Machine Learning’, etc. 

A resume summary outlines your professional experience and achievements in 2-4 sentences. A resume objective outlines your potential interests/goals that you wish to achieve professionally in 2-4 sentences. 

In a small paragraph, you have been able to tell the recruiter a lot about yourself, what you bring to the table, and how you aim to improve your professional career. 


Work Experience


As mentioned, a reverse-chronological order is always the best approach. The recruiter wants to know the last thing you do and how it can be applied to the company. You should mention

  • Your position at the company
  • The company name
  • The timeframe/dates you were working at the company
  • Your responsibilities and achievements

When it comes to mentioning your responsibilities and achievements, always go one step further on how you explain this. For example, rather than stating ‘improving data accuracy’, you can say ‘improving the accuracy of predicted data by 22%’. This not only shows the recruiter that you have the skills in improving accuracy but also to what extent. 

An example of formatting your work experience can look something like this:

Data Scientist

Company X

01/2019 - 01/2022

  • Developed end-to-end Machine Learning prototypes and increased the efficiency by 20%
  • Proposed and implemented a new targeting model, generating $750k in sales
  • Developed action plans to improve the decision-making process by using data science


If you are fresh into the world of Data Science, you may not have any experience. However, you would have done some project work at University, Online courses, BootCamps, etc. This would be a good place to state those. 

Another way you can show your experience as a newbie is 

  • to start doing some freelance work
  • To work on some open source projects
  • To work on personal projects, using platforms such as Kaggle. 




There are hard skills and there are soft skills. Although many Data Science roles are technical skill-based; soft skills are still very important. 

Typical hard skills for a Data Scientist include

  • Data Wrangling/Cleaning
  • Data Analysis
  • Data Visualization
  • Statistics
  • Mathematics
  • Probability
  • Machine Learning
  • Modelling

Typical soft skills for a Data Scientist include:

  • Communication
  • Problem solver
  • Business Acumen
  • Research
  • Time-Management
  • Team Collaboration
  • Independent 
  • Critical Thinker




Education is an important element of your resume. It lets a recruiter know what you’ve studied and how you got to where you are now. With Data Science, in particular, there are so many BootCamps and courses you can take, these should be stated as they are all part of your education. 

Your education needs to include:

  • Type of education; degrees, masters, Bootcamp, etc
  • Major; Computer Science, Data Science, etc
  • University/Institution name
  • Years studied
  • GPA, Honours, etc

The format should look something like this:

BSc in Computer Science - Columbia University

2016 - 2020

  • Course included: Linear Algebra, Statistics, Applied Modelling, SQL, Machine Learning, Computer Science, and Production Deployments


The Other Important Aspects of a Data Science Resume


The differences between a normal resume and a technical data-related resume is projects, achievements and certifications. 




This sector is one where you never stop learning, therefore you will catch yourself taking additional courses. These courses will prove to recruiters that you have taken the initiative to develop your learning; which is highly noticed. 

The structure of this may look something like this:

Awards and Certificates:

  • 100 Python Problems – Udemy
  • IBM Data Science - Coursera Certificate                                                                               
  • Automate the Boring Stuff – Udemy




If you don’t have any work experience in the field, your projects should explain the skills you have and how you have applied them to your projects, such as end-to-end machine learning. If this is the case, you might realise that most of the content on your resume will be focused in the project area. 


Interests and Hobbies


Although these may not be technical related, stating your interests and hobbies will let the recruiter know more about you. It also lets data team managers know if you can be easily integrated in the team and if your interests and hobbies could be of benefit. 




If you’re actively searching for Data Science roles, consider refining and tuning your current resume to ensure that your resume is getting noticed by technical recruiters.

Remember, 7.4 seconds is how long a recruiter looks at each resume; so make sure it counts.

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.