Data Science Resume Examples

Data Scientists are in high demand, and employers want to find candidates who have the right skills.

Since 2012, Data Scientist roles have increased by 650 percent, and this rise shows no sign of stopping. The U.S. Bureau of Labor Statistics predicts that the demand for data science skills will increase another 27.9 percent by 2026.

A well-written data science resume highlights a candidate’s accomplishments and achievements. Professional resumes show that a job candidate has proven skills and that they can help a company achieve its goals.

Data science resumes are documents that provide a brief overview of Data Scientists’ (or aspiring Data Scientists’) qualifications and experiences. Your resume is your first impression with Hiring Managers, who will often only look at resumes for 30 seconds.

For entry-level data science jobs, it is particularly important to first do some planning and preparation before you begin writing a data science resume. This goes for Senior Data Scientists as well to ensure you include up-to-date examples that highlight recent projects.

Here are a few steps to follow before you start writing your resume and some sample resumes to help land that dream data science job.

Resumes should be tailored to the position and company you are applying to. Browse the company’s website and social media to get a better sense of their work, mission, and values. Then, consider how you can add value as a Data Scientist. Remember to also carefully review the job description and take note of the required data science skills—you will want to emphasize these on your Data Scientist resume to demonstrate your value and skills to the hiring manager reading your resume.

Concisely communicating why you’re the right person for a job is a science in itself. An elevator pitch is a brief synopsis that describes your background, credentials, and what makes you a unique candidate. When you have your pitch, make sure to refer back to it as you write your Data Scientist resume. This can help you prioritize your most important data science skills and accomplishments.

Select a resume template that employers and hiring managers can easily skim. While you can be creative, a simple resume design or resume template tends to work best. It is important that the design does not distract from the content of your resume. Include ample white space, proper headers, and consistent formatting.

Consider the data science skills and projects that are most relevant for the particular position you are applying for. Focus on showcasing these in your resume. Select projects that demonstrate your technical data skills, as well as how you helped solve a problem. Create a list with the specific skills, tools, and programming languages used for each project.

After you complete your planning, you can move on to drafting your data scientist resume. As you begin writing, there are a few best practices to keep in mind.

• Be concise: Data science resumes should be roughly two pages long. Employers may be reviewing hundreds of applications, so only include your most relevant data science skills and experiences. Professional resume formats and resume templates are great resources to help keep your important information below the page count.
• Use bullet lists: Bullet points keep your resume organized, easy to read and draw attention to key terms and attributes.
• Use action verbs: Choose simple, purposeful action verbs that highlight your accomplishments and explain your contributions to a team or project. Examples of action verbs include: constructed, solved, accelerated, reduced, and launched.
• Use numbers and key metrics instead of generic adjectives: Avoid adjectives like “strong” or “experienced.” These words lack specificity and substance. Instead, use concrete metrics and specific examples to showcase your achievements. Quantify your accomplishments so employers can clearly see the value you can bring to their team.
• Write specific, powerful accomplishment statements: These statements describe what you have achieved in your career. A general outline for data science accomplishments statements are: Action verb + task + result. For example, “Developed new forecasting models which increased company efficiency by 50 percent.”
• Don’t bury the lede: Emphasize your most important and relevant experiences at the top of each section or heading.
• Highlight past projects: Include relevant work and data science projects on your resume that display your skills and make you stand out. Project work is particularly useful if you do not have many years of experience.
• Simplify jargon: WWhile you should include relevant technical keywords, avoid overloading your resume with jargon. Some Hiring Managers may not have a technical background, so make sure they can still understand your accomplishments.
• Edit and proofread: Do a careful spell check and grammar check. Show employers that you are thorough and detail-oriented. A second pair of eyes is also useful, so ask a friend or peer to review your resume.

The purpose of a data science resume is to provide an overview of your experiences, skills, and accomplishments as a Data Scientist. The resume is your introduction and pitch to an employer. Resumes tell the story of your career in a brief and organized format. They highlight your relevant accomplishments and show the value you can bring as a Data Scientist.

Ultimately, the resume can help you move forward in the job application process and secure an interview. In the interview stage, resumes also act as reference documents for the hiring team.

To create an organized data science resume, you can follow this general outline:

• Contact information: Include your name, phone number, email, LinkedIn, and GitHub, or portfolio.
• Profile/Summary/Objective: In two to four sentences, outline why you are the best candidate for the role.
• Experience: Outline your relevant data science experience.
• Projects: Highlight relevant data science projects.
• Education: Include degrees/certificates, along with the school and graduation date. You can also highlight relevant coursework and academic achievements.
• Skills: Include relevant technical data science skills that match the job description.
• Additional Sections: This can include conferences, publications, awards, and other activities or interests.

In your data science resume, include a profile, work experience, education, skills, achievements, and extras.

• Profile: A strong profile (also called a summary or objective) will help your data science resume stand out. Your profile should tell a story. Include a brief description of why you are a good fit for the Data Scientist role. If you are switching fields, explain your move into data science. Some job postings will not ask for a cover letter, so this is a good section to show your enthusiasm for the position and company.
• Work experience: List your most recent and relevant work experience in reverse chronological order, with your most recent data science experience listed first. Each experience should include your job title, the company, the period of time in the role, the location of the job, and your main accomplishments. Aim to have two to three bullet points for each experience.
• Achievements: When listing data science experience or projects, focus on emphasizing your accomplishments rather than the tasks or responsibilities. Where possible, include numbers and metrics to quantify your successes. For example, instead of “Forecasted company sales,” rewrite it as, “Used predictive analytics to forecast company sales with a 95 percent accuracy rate.”
• Education: Include educational details with your most recent education first. List any post-secondary degrees, whether or not they relate to the data science role (some Data Scientist positions may require a degree in any field). Include your degree type, major/minor, school name, and date of graduation. Also, list any courses or certifications you received outside of college or university. You can also highlight relevant coursework, academic achievements, scholarships, and extracurricular activities.
• Projects: List relevant data science projects and include the title, a link, and your role in the project. Briefly describe the project and include relevant tools/programs and skills.
• Skills: Include relevant technical skills, with your strongest data science skills listed first. Review the job description and match your skills with the ones required for the position.
• Extras: Additional sections can showcase further accomplishments that make you stand out. This section can include conferences, publications, awards, interests, and participation in hackathons. These can show your passion and dedication as a Data Scientist.

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. In your resume, list skills that match those in the description. You can also add a few additional skills that you think are related or relevant, or that will help you stand out.

Some of the most important skills for Data Scientists include:

• Data analysis
• Data wrangling
• Data modeling
• Statistics
• Data visualization
• Programming
• Quantitative analysis
• Machine learning
• Machine learning models
• Data mining
• Debugging
• Hypothesis testing
• A/B tests
• Regression

• R
• Python
• C
• C++
• C#
• HTML
• Java
• JavaScript
• PHP
• SAS
• SQL
• Scala
• MATLAB
• SQL Server
• NoSQL
• Hadoop
• OpenRefine
• TensorFlow
• Cloudera
• Tableau
• Microsoft Excel
• Octave
• Spark
• PowerBI
• Plotly
• Bokeh
• Matplotlib
• Seaborn
• Keras
• Pytorch
• AWS
• Hive

AME Phone Number Email LinkedIn GitHub / portfolio link

Junior Data Scientist with [#] years of experience in [area of expertise]. [Major data science accomplishment or project]. Skilled in [top skills]. Passionate about [data science-related passions].

Job title, Company Month, Year – Month, Year

• [Action word] [skill/task] [result/impact]
• [Action word] [skill/task] [result/impact]
• [Action word] [skill/task] [result/impact]

Job title, Company Month, Year – Month, Year

• [Action word] [skill/task] [result/impact]
• [Action word] [skill/task] [result/impact]
• [Action word] [skill/task] [result/impact]

Degree, School name Graduation date

• [Relevant courses]
• [Academic achievements]

• Project, Role
• Brief description of project

• [Technical skills]
• [Software, tools]

• [Hackathons]
• [Volunteer experience]

As a Data Scientist, you are expected to work with big data and data sets, identify relevant data, and then make informed decisions and recommendations to solve business problems.

A Hiring Manager’s business problem is finding the right Data Scientist to fill an open position. If you approach writing a resume with the same approach as you would while analysing data, you will put yourself in a great position to create a stand out data science resume and cover letter.

To recap what we’ve described above, make sure to follow these resume-writing steps:

• Research the company, the role, and relevant data skills
• Reference resume templates and samples to build a resume outline
• Add relevant education experience, work experience and data projects to the correct section of your resume
• Highlight experience with machine learning and data tools
• Craft concise bullet points using the action verb + task + result format for each experience, emphasizing data-driven successes
• Have a trusted peer proofread your Data Scientist resume for grammar and spelling to make sure your experience is professionally presented