How to build a great Data Science resume in 2020?

Are you looking to crack a Data Science job in 2020?

If you think you are equipped with enough skills and experience to bag the role of a Data Scientist, all you need to do is get out there and start an active hunt for these roles.

However, there are hundreds of aspirants applying for the same role as you. In this case, how do you stand out from them?

While there are many aspects to building a successful professional profile, in this blog, we will talk about the need for a lean, effective and impactful Data Science resume.

Opportunities in Data Science

India’s share in the open job positions all over the world is 6%. Most of the job openings in the data science field are full-time (97%), while not much (3%) are contractual or part-time. 

Do you know how many total jobs are there in the field (as of February, 2020)? The number is huge- 97,000!.

How are aspiring Data Scientists applying for roles of their choice?

This is an important question to understand your competition.

Data Science practitioners are actively using a host of portals to get into their desired roles an enhancing their profiles to better position themselves.

Job aspirants prefer different modes to search for their ideal job; you might prefer LinkedIn, a professional way but your acquaintance might opt for contacting his connections in the industry. 

51% candidates prefer LinkedIn to search for a job in this field., the favorite job portal, holds the second slot with 18%. 

This is followed by 19% of the people who make use of other job portals and mediums. The remaining 12% of the people wish to contact acquaintances and friends to know about the job opening.

Whatever be the mode of application for these roles, what stands out is that one of the first points of engagement between the candidate and the hiring manager is the resume.

Resumes are a quick and effective way to shortlist candidates and form an initial opinion of their experience, skill-set and presentation skills.

So it not only matters what you put on your resume, it also matters how you put it.

Let’s take a closer look.

Before anything else, remember you will have to create multiple versions of these resumes for better results. 

Always start with a Master resume, which is nothing but an umbrella resume with all possible information about yourself.

Subsequently for each job, you might want to tailor your resume and edit to keep only the relevant information.

Components of a Data Science Resume

A Data Science resume will have the following parts:

  • Summary or Description
  • Experience
  • Education
  • Skills
  • Projects
  • Certifications
  • Contact Information

These sections will help you showcase your background, as well as the knowledge you have in relevant fields.

Including both an Experience and Projects section will help the recruiter evaluate your experience as well as allow you to highlight specific ventures you’re really proud of working on.

How to build a great Data Science resume in 2020?

How to choose a Data Science resume template?

There are many options available on the internet right now. A simple search will confuse you as to which template will best enhance your options.

When choosing a template, remember, decorative templates do not add much value to your profile.

Keep your choices limited to modern, crisp resumes with distinct formatting between headings and body text. However, at the same time, the formatting should not be too grand or dramatic.

  • Choose a template that complements your content – look for the kind of formatting that works well with the amount of content you’ll put on the resume.

If you’re a fresher with not much experience, do not fall into the pitfall of a largely empty resume. Similarly for experienced professionals, do not overcrowd your resume.

  • Everything over 1-2 pages is too lengthy and dull; try to fit your resume in 1-2 pages at most. Don’t hesitate to cut out experience that’s not that relevant to the position you’re applying for.
  • Avoid long text – no bullet point should be longer than 2 rows. 
  • Choose fonts that look professional. Stick to something clean and professional.
  • You can choose to add some color, but don’t overdo it. If you do not understand color schemes well, best to skip this.

1. Summary/ Description

Resume summaries are a great way to brief your hiring manager of your professional journey.

A resume objective is great for an entry-level data scientist who wants to show their passion for the subject.

A summary is also great when you’ve transitioned into data science from another field. Use active voice with high-achieving, action verbs to stand out in this section.

2. Experience

This is one of the most prominent sections of the resume and needs extra attention!

Include only major and relevant positions. All positions held should clearly be listed with the name of the designation, company and tenure. (months and years)

Make it reverse-chronological with proper start and end dates. So add your most recent positions first.

Focus on impact rather than responsibilities – data mining, statistical analysis, and data visualization will be on pretty much any data scientists’ resume. The question is what was the impact of your work on the business. So explain that rather than just listing responsibilities.

3. Education

Your educational background matters a lot more in a resume if you are a fresher with not much experience.

Make sure to give this section the attention that it deserves given how relevant education is to your profile. 

What to include here:

  • Name of university and course
  • GPA and final marks
  • Key subjects relevant to the position you’re applying for
  • Any awards you received or societies you were a part of

4. Skills

The skills section is another conveyor of why should the hiring manager consider you for the role.

It’s more crucial in shortlisting the candidate than anything else.

Remember to enlist all your technical knowledge here. Soft skills can also be added but it is more tangible and impactful to add your technical know-how rather than something like communication skills!

Here are examples of skills that will distinguish you from other candidates:

  • Programming languages including Python, R, and Java
  • Quantitative and statistical analysis tools like SAS and R
  • SQL databases 
  • Data visualization tools like QlikView and Tableau
  • Python Libraries and Packages

How to build a great Data Science resume in 2020?

5. Projects

If you have worked on specific projects, be it during your education or as freelance projects, list them in the Projects section. You could also add here key projects you’ve worked on in your past work positions.

Whatever you do, make sure to not only explain what the project was about, but also show what the impact of your work has been.

Numbers are very important here. Let the results of your projects speak for you.

6. Certifications

This section helps certifications to stand out. Most professionals opt for online certifications to learn Data Science and other related subjects rather than enroll in courses at universities.

Add any capstone projects you worked on – certifications usually make you show what you learned in practice. Mention your capstone and other projects you’ve worked on as part of the course.

Mention the start and end dates of the certification along with the period of validity.

7. Contact Information (Part of Header)

Keep your contact details short and to the point.

No one needs to see personal details like marital status.

This section should strictly include the following information:

  • Specific information about what stage of career you are (preferably current position)
  • Contact information including phone number and email
  • A link to your LinkedIn profile
  • A link to a personal Github or website to highlight your previous work.

Formatting Skills

Once the content for your resume is decided and secured, the next step is presentation.

Great formatting can help you augment your resume to outshine the competition.

Stay committed to your style of formatting throughout the length of your resume. Keep placement simple and easy to read; too many columns and sections take away ease of readability.

Depending on the quantity of content in your resume, choose a suitable margin size and font size. Do not make the text too small to read and an oddly larger text will make your resume tacky.

Choose simple, readable fonts from the sans serif family. Font color should be kept black for consistency. However, you can play with this if required.


We cannot emphasize on how important it is to proofread your resumes 10 times before forwarding. Nothing is more off-putting than spelling or grammar mistakes in a resume.

They make a bad first impression and it is rarely possible to recover from this. Consult a friend or mentor if need be but do not forward a resume that has not been proofread and scanned thoroughly.


These were our pointers for building outstanding Data Science resumes in 2020. If you need help with any aspect of resume building, write to us in the comments section.

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