How To Write a Data Scientist Resume (With Example)

By Indeed Editorial Team

Updated November 21, 2022

Published September 29, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you're applying for a data scientist role, you typically need to submit documents as part of your application. A resume is essential to showcasing your experience and the value you can bring to the company. Learning how to write an effective data scientist resume can help you get an interview and increase your chances of being hired. In this article, we explore what this type of resume is, present the steps to writing an effective resume, and provide a helpful example to guide you.

What is a data scientist resume?

A data scientist resume is a document that outlines your educational background, skill set, and work history in data science. Recruiters typically request this information before deciding whether you qualify for the next stage in the hiring process. As there could be many candidates applying for one open position in this field, it's important that your resume is memorable.

How to write a data scientist resume

Follow these steps to write an effective resume:

1. Select a resume format

Here are two popular formats you can use to create your resume:

Functional: This format focuses on your skill set and lists it before your experience section. Functional resumes are typically suitable for recent graduates or professionals changing careers into data science. You can also use this resume format if you have a large gap in your work history.
Chronological: This format focuses on your data science experience and places it in order. You can write a chronological resume if you have consistent work history as an entry- to mid-level data scientist.

You can also use a combination resume format. This format combines functional and chronological resume types. Consider using a combination resume when applying for executive-level data science roles to outline your career achievements or highlights.

2. Include your contact information

A contact information section makes it easy for employers to get in touch. Consider including your city, province, or territory, along with your postal code. Next, make sure you provide your phone number and email address. Add a link to any recent data science projects to give hiring managers and recruiters an idea of your abilities. This can also show whether you're actively working on projects that may be useful to the company.

3. Write your resume objective

A resume objective, or a resume summary, is a section that summarizes your experience, career interests, and qualifications. This section can be used to convince hiring managers that you have the required data science experience and are passionate about the growing field. Ensure your resume objective aligns with the job requirements to show that you can contribute to the company's goals. By describing your career goals clearly, potential employers can envision how you fit into their company.

4. Highlight your skills

Working as a data scientist typically requires you to perform independent research, analyze large data sets, and interpret data. The role usually involves statistical methods and machine learning techniques to understand data patterns and trends. On your resume, highlight in-demand skills that you would expect to apply if hired. Here are some hard skills for successful data scientists:

A/B testing
Big data
Cloud computing
Computer programming
Computer vision
Data analysis
Database management
Data mining
Deep learning
Graph algorithms
Machine learning
Natural language processing
Predictive analytics
Query languages
Regression analysis
Statistics and probability
Sentiment analysis
Visualization

You can also include your technology stack when discussing your skills. Your stack refers to the tools and frameworks you use to solve data science problems. Essential soft skills for data scientists include:

Attention to detail
Analytical skills
Communication skills
Critical-thinking skills
Problem-solving skills
Research skills

5. Present your work history

After you've listed your skills, detail your professional experience in the field. Employers typically want to see the projects you've worked on and their business impact. When outlining your work history, consider using bullet points to make your resume more readable.

6. Outline your educational background

While you can gain entry-level employment without a degree, employers may require you to have postsecondary education. Consider presenting your formal education at the top of your resume if you recently graduated. As you progress in your data science career, employers will often value your practical experience more. You can include the name of your university or college, your degree, graduation year, GPA, awards, and even courses.

7. Consider adding relevant certifications and data science projects

As data science certifications demonstrate expertise, they can help you stand out. If you're applying for a data scientist role, validate your skills by including any certifications you've earned from reputable sources, such as leading software corporations. You can also include projects that demonstrate your knowledge of data science principles.

Tips for writing your resume

Here are some tips to help you create an effective resume:

Use a professional format

Ensure you choose a readable font, such as Arial, Georgia, Calibri, Times New Roman, or Helvetica. You may use between 10- to 12-point fonts for your resume content and 14- to 16-point fonts for section headings or any information you want to emphasize. A professional format makes it easier for recruiters and hiring managers to review your resume.

Prioritize the company's needs

When writing your resume, ensure your focus is on demonstrating how you can add value to the company. While you're encouraged to show versatility through a wide range of skills and duties performed, confirm they are relevant to the job. For example, if the job description states you would work primarily on machine learning projects, consider creating a resume that emphasizes your expertise in machine learning.

Be honest and concise

Make sure your resume accurately depicts your data science skill set and professional experience. This can help you earn your potential employer's trust. Also, create a resume of one or two pages. You want to write the most important details about your work history and qualifications first. Any other information that helps your application can be included with a link to your personal website or a hosting platform for your programming code.

Use impactful data science terms

Review the job posting to find keywords you can use in various sections of your resume. Action verbs and adjectives can encourage recruiters and hiring managers to continue reading. Consider using the following words on your resume:

Analyzed
Applied
Built
Cleaned
Deployed
Developed
Extracted
Explored
Implemented
Modelled
Predicted
Researched
Trained
Validated

Proofread your resume

As a data scientist, you need to be detail-oriented and skilled at communicating with other data science professionals. Proofreading your resume is one way to show potential employers that you have excellent written communication skills. You can have a friend, career coach, or data science mentor revise your first draft.

Data scientist resume example

Here is an example of a functional resume for a data scientist:

Andrew James
(245)-777-8888
a.james@email.com
codehostingsite.com/jamesandrew
Miramichi, NB

Summary

Data scientist with excellent computer science background and one year of experience performing predictive analytics, data processing, and data mining for small- to large-size companies. My career goal is to help companies gain insights from large data sets and contribute to the open-source community.

Skills

Data analysis and visualization

Trained in examining data, finding patterns and trends, and visualizing data sets

Machine learning

Skilled in supervised and unsupervised machine learning. Excellent at building both regression and classification algorithms, such as k-nearest neighbours (KNN), support vector machine (SVM), and decision forests.

Computer vision

Skilled at training computer systems to gain insights from digital images and video inputs. Trained in building and deploying deep learning algorithms, such as artificial neural networks (ANN), convolutional neural networks (CNN), and deep belief networks.

Natural language processing

Experienced in building sentiment analysis models using Python and Spark. Skilled at scraping internet sources and developing customer support internet bots.

Predictive analytics

Skilled at predicting values, such as the time to failure of machine equipment, customer churn rate, and accident frequency. Experienced in data cleaning, modelling, and mining.

Communication skills

Experienced at creating data science presentations and reports for machine learning engineers and upper management

Experience

Goody Company, January 2021 - August 2021

Junior Data Scientist

Built and deployed a machine learning model for a marketing agency, which improved sales by 14%
Developed a predictive maintenance model to determine the remaining useful life of a pulverizer, which reduced downtime by 35% and maintenance costs by 23%
Analyzed sales data using SQL

Education

University of Hannington, August 2015 - June 2019
Bachelor of Computer Science

Research assistant to Prof. Jane McDonald on "Condition-based maintenance model for heavy-duty generator sets using vibrational analysis"

Community High School, September 2011 - June 2015
High school diploma

Please note that none of the companies mentioned in this article are affiliated with Indeed.