Best Data Science Resume Tips in 21st Century - Data Trained Blogs (2023)

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(Video) This Is The Perfect Resume For Data Science Role

Introduction to Data Science Resume

Data science is the buzzword of the decade. This multidisciplinary field dealing with data attracts millions of talented people from diverse fields. Due to its diverse nature and ubiquity, thousands of jobs are generated each year. According to a LinkedIn blog “Now, virtually every industry, from retail to manufacturing, is collecting data on their customers. That’s causing a surge of demand for data scientists who can best interpret all that data.”

However, most industries are facing a dearth of data scientists every year, despite the existence of a huge talent pool of interested individuals. An important factor causing this gap is a poorly written data science resume. As this sentence from a Glassdoor blog succinctly puts it, “(A data science resume) is the first, and sometimes, the only thing that recruiters and hiring managers to turn to in order to determine whether or not you’re a fit for the job. And if you phone it in, your first impression may be your last.”

Data scientists dig through data for a living, but it can be a living nightmare for a recruiter when they have to dig through scores of lackluster data science resume just to find the right one. A typical recruiter for data science is inundated with numerous data science resume each day. They have to decide whether to hire someone or not just by taking a glance at the resume. A perfect resume helps you to hit the bull’s eye by helping the recruiter decide whether your resume is worthy of an interview, just by taking a look at it.

At first glance, a perfect data science resume seems to be impossible to write. If you think so too, you are not alone!

Most people don’t have a clue as to what makes a data science resume good. We tend to dump as much information about ourselves as possible in this short document. It is imperative that the resume is pitch-perfect, i.e. it portrays a colorful picture of you as an ideal fit for the position you are applying for. It is important to have the relevant skills to build a resume that can portray the best of you. Our tips for building the perfect resume for data scientists will help you do just that.

Here we break down five steps to make your Data Science Resume the perfect fit.

1. Find out more about the job

This is the most interesting and fun part where you get to be creative. Imagine yourself as a recruiter looking for a data scientist. What skills would you look for out of the possible candidate? Try and write a job description for the ideal candidate for the job. Get creative – be as detailed as possible!

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Read through job descriptions relevant to the post you are applying to, and pick out similarities between them. Resources like LinkedIn and Glassdoor can be handy in this regard.

It is also important to know what a company expects from a potential employee. The company website is a really useful resource to know more about its values and what direction it expects to grow in the next few years. Going through the “About” sections of the company website can be amazingly insightful.

Once you have collected all the information, go back to the job description you wrote and re-evaluate it. Keep it aside as it will come in handy down the line as a reference.

2. Choose a suitable design template

A template should be plain and minimalist. Remember, less is more when you choose a data science resume template. It is meant to convey information at a glance. Choosing a fancy template with colorful fonts can be too distracting and make the resume lose its purpose. provides a number of clean data science resume templates to choose from.

What a recruiter expects from a data science resume is for it to be simple, such that the employers can skim through it and get an accurate idea of your skills and expertise. Creativity is encouraged. In fact, a little creativity with the organization of the content and design even helps catch the recruiter’s eye. But there is a fine line to be drawn here.

3. Organize the content

Once a suitable template is selected, you need to organize the information into relevant sections. If you are applying for more than one company, it is important to customize the data science resume as per the company’s requirements.

An easy way to do this is to organize sections in the order of preference. Then you may start adding any relevant bullet points to the individual sections. This can be easily done by making a few notes on each section. For instance, under “Experience” you can write notes like “Created and presented models for a capital management firm and affected 15% better returns against previous performance.” Do repeated brainstorming to add specifics to the points.

Once this is complete, the content needs to be organized in such a way as to draw the author’s attention. Start by pitching the most interesting, the most important points in each section. This keeps the recruiter interested and makes their evaluation of your data science resume easier.

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You can tailor the lengthier sections by removing any redundant bullet points into a short, 1-2 page version containing all the necessary information for the specific position you are applying for. This saves time and helps organize ideas into a cogent, coherent data science resume.

4. Fill in the specifics

It is important to keep in mind that the data science resume should be designed to land you in the interview. Landing the job is secondary. Thus the content of the resume, starting from the experience to the projects to the programming languages, needs to be highly selective and impactful in order for the recruiter to see exactly what they need in you.

Education and certificates

Education justifies the level of skill that a person has. A bachelor’s degree in data science is often enough for many entry-level positions, but adding certificates, extra-curricular or relevant coursework is an added bonus and balances out the required skill set for an entry-level candidate.


Include any past projects that you may have done. Specific points make for an interesting read and allow you to display the application of your skillset into actual experiences. This will be especially helpful for new candidates, compensating for the lack of experience.


Experience is the most valuable asset of the candidate. Keeping it relevant and concise at the same time should be kept in mind while highlighting any relevant experience. An internship would be especially helpful for recent graduates who are about to enter the data science industry.

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Example from the experience section from a data analyst’s data science resume :

• Successfully interpreted data in order to draw conclusions for managerial action.

• Utilized statistical methods to test hypotheses to validate data and interpretations

Technical skills

Technical skills are important in data science, as applying to a few companies may often need you to highlight any programming language skills that may be unique to them. However, instead of just blandly stating the programming languages you know, weave them into narratives involving your past work or projects. For example, you may write something like “Halved training time by using regularized regression in R”. This makes for a more interesting and engaging read.

5. Tweak your content

Now it is time to separate the wheat from the chaff. Remember when we put ourselves in the recruiter’s shoes and wrote a job description? Well, it is time to invoke that and read through it so that you can benchmark your data science resume against it. Remove any redundant points and cut down on any unnecessary fluff. Your data science resume needs to look perfectly suited to the position, and it is important to review it so that it looks tailor-made as per the requirement of the position you are applying for.

Most of the time a recruiter will prefer a data science resume with plain language instead of technical jargon in favor of readability. Wherever possible, pitch your skills with a comfortable level of detail and reduce the technicality. Not a lot of detail is required, and often it is the case that the lesser detail you put, the more succinct you appear.

Have your data science resume be read by two or three of your friends. This way, a person with a fresh perspective can look at your data science resume and identify the pivotal points of your pitch. That way the main points of the resume should stand out and you can change them to suit your needs.

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Remember that your data science resume is just the first step to the application process, and must be designed in such a way as to land you an interview. A perfect resume showcases the set of skills and experience that make you an ideal match for the job. Once you land an interview, the next logical step is to ace it! Ensure that you have all the skills you need to ace the data science interview, and our article on “How to prep for Data Science Interviews” is tailor-made to do just that!.


How do I add skills to my data science resume? ›

Data science resumes should include technical skills that are relevant to the position you are applying for. A good strategy is to first list all your data science skills, including any software and tools. Next, review the job description and highlight the skills that are required in the role.

What are all four major skills need to be a data scientist? ›

You need to have knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles. These programming languages help data scientists organize unstructured data sets.

Can I add DataCamp projects to my resume? ›

Putting DataCamp certificates in your resume is beneficial, and if you don't know how to put DataCamp on your resume, you can use my ideas. Below are some of the ways you can make your resume stand out in any job application: Add relevant skills to the skill section of the resume.

What skills should I list on my data science resume? ›

Data scientists must list their expertise on their resumes. Some commonly sought-after skills include data analysis, data wrangling, data mining, data visualization, statistical analysis, machine learning, predictive analytics, and programming.

What is a good data scientist resume for beginners? ›

Entry Level Data Science Projects for Resume

Give a short project description. Mention the tech stack of the project. List your roles and responsibilities along with some of the accomplishments of the projects with figures.


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