The Perfect Pitch: How to Curate an Effective Portfolio for a Data Scientist Job


Your portfolio can be the first chance to get you noticed bya potential employer. What makes it perfect is to get the right skills in the right place.

There’s no clear-cut formula in getting your data scientist portfolio right, but there are certainly a few pointers you might need to consider.

Ultimately, the idea of optimizing your portfolio is to draw the attention of the employers looking to hire. Remember, the companies want to see whether you’re good enough as mentioned in your resume. They want to know whether you have everything it takes to earn a data scientist job at their firm. They need to be sure.

A data scientist CV or portfolio should always be unique with the corresponding organization he or she is looking to interview with.

To get this right, we will explain what an individual needs to add in their CVs.

We’ve compiled what employers expect to see in your data scientist CV

  • Remember to place the right set of skills first

You can list the set of relevant skills right at the top of your IT resume, this saves the employers time of scanning it through the complete document. However, it should be mentioned in a way it catches the attention of the employer.

Avoid using bullet listing. This may just eat up space on your resume. Try to highlight it differently, ask the designer to design it in a way it saves space, and list them in a single sentence.

For example,


Data science, R, Python, data analysis, statistics, predictive analysis, machine learning, artificial neural networks.

Ensure you have the most important skills mentioned at the start and move to the least impressive skill. Don’t forget to mention the skill level – beginner, intermediate, or expert along with the skills.

Takeaway – Listing the skills at the top gives the employer an idea of whether they’re hiring the right candidate for the job.

  • Do not list your non-technical skills, but explain

If you’re applying for a data scientist career, it is recommended to explain the skills and not present the skills in the listicle. We’ve come across many resumes and CVs of data scientists where their non-technical skills were highlighted in bullet points. This is a mistake, you mustelaborateon them.

For example, “verbal communication.” What does this explain your skills to the reader? Don’t you think the hiring manager will be confused?

Instead, try and follow the below examples.

Non-technical skills Description
Storytelling Demonstrated the results of data analysis through data visualization presentations. This makes it easy for customers to understand the results.


Verbal communication Conducted weekly meetings with clients and updated the status of every ongoing project.


Adaptability Managed both data visualization projects while working on machine learning to distinguish problems.


Doesn’t this look much appealing?

Communication and teamwork are among the crucial non-tech skills a data scientist must possess.

A 2019 report by the State of Data Science says, nearly 60 percent of data scientists either work in a team of five people or more.

Do not make the mistake of writing skills without explaining.

  • A concise and detailed overview of your achievements

This section is another critical aspect you need to be careful about. This is the section where you need the “show” and not the “tell” part a potential employer needs to know about you.

Before they could break the question of understanding what your flaws are, impress them with your strengths.

Here’s how you can divert their attention:

  • Use numbers – numbers tend to gain a lot of attraction to the employers. This helps demonstrate your impact in a better way.
  • For every achievement, use a single sentence – your CV should be complete on one page, therefore try limiting your achievements in one-liners.
  • Mention only relevant achievements – focus on data science achievements. E.g. Helped the students with their data science dissertation to improve their academic skills.

Remember getting through an interview for a data scientist job is no joke. A great CV and portfolio make an impact.

  • Ensure you have the right keywords that match the job description

Even data science employers use keywords in job descriptions. The data science industry generally uses certifications, technologies, name of the skill, and qualification in their job description.

Job postings and requirements are stuffed with keywords relevant to the employer. This means if you’re applying for a data scientist job you need to make sure you use the right keywords.

Note:Make sure not to stuff your portfolio with keywords.


To sum it up, a data scientist CV/portfolio does not need to be flashy or fancy. You just need to present it in a way it impresses the employer at the first glance itself. If you manage to do this, you’re halfway there.

The right CV could determine your next job!