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Data Scientist Resume Samples

Entry-level candidate seeking data scientist job

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Recent university graduate with an M.S. in Engineering; experience leveraging statistical analysis and critical reasoning to automate crucial workflows.

• Conducted data analysis alongside professionals during internship with SaaS company
• Proficient in High-Performance Computing (HPC) and C# and python languages

Experienced candidate seeking data scientist job

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Experienced professional backed by more than a decade in the field, competent in the use of powerful data mining algorithms and building statistical models that drives change and innovation across various products and customer experiences.

• Secured stakeholder approval across three continents and developed a unique algorithm for use in data mining applications.
• Collected raw data from the internet using in-house web scrapers and built statistical data analysis presentation used in developing a new product revamp.
• Managed a team of four data scientists, one of whom advanced into senior role.

Data Scientist Resume Vocabulary & Writing Tips

The words you use in your resume can help you to stand out among other candidates. Carefully examine the job listing or description to discover keywords that the hiring manager or resume screening software may be looking for.

To help you get started, we’ve compiled the following list of industry-specific terms and action verbs.

When completed, remember to thoroughly check your resume for common mistakes, such as errors in spelling, grammar, or punctuation.

Words to Use

• Computer science
• Modelling
• Statistics
• Analytics
• Insights
• Data sets
• Data wrangling
• Data-driven solutions
• Data mining
• Algorithms
• Machine learning
• Outlier detection
• Trends

Action Verbs

• Discover
• Develop
• Predict
• Achieve
• Experiment
• Explain
• Analyze
• Perform
• Conduct
• Utilize
• Organize
• Update
• Modernize
• Improve
• Infer
• Forecast

Data Scientist Resume Tips and Ideas

Data scientists are designers and inventors at heart. They develop solutions involving computer hardware and software and are in high demand from companies everywhere.

A data scientist uses their knowledge of computers and engineering processes to create or apply new technology, applications, and programming.

To have a top career in data science, you need to demonstrate near-expert problem-solving and analytical abilities. Also, in-depth knowledge of the workings of computer systems, programming, and maintenance is essential.

The most common qualifications for this role are:

1. Knowledge of database management software
2. Experience with machine learning
3. Scripting language skills (python, Ruby, C#)
4. Teamwork and collaboration

Most professionals in data science have a master’s degree in a computer-related field and extensive technical proficiency.

Employment of computer and information research scientists is projected to grow 23 percent from 2022 to 2032, much faster than the average for other occupations.

Format

Reverse chronological order

Design

When designing your resume, consider the use of infographics or icons

Photo

No

Sections

Required:

• Contact information
• Education
• Technical skills
• Work history

Optional:

• Objective statement
• Soft skills
• Languages

Resume Length

1-3 pages

Format

Functional resume formats are common in highly technical careers like data science because they focus on your skills before your experience. List your skills – and the contributions made using those skills – first.

However, the reverse chronological format is the most commonly accepted format by both recruiters and ATS software. This lists your most recent experience first, working backward.

Keep in mind that functional resumes are not common in the U.S. Recruiters may be more prefer a combination resume. This format is especially useful if you have a long history in the career field.

Here, highlight your abilities in a skills list followed by a reverse chronological listing of your work experience and education.

Design

It is important to present a resume that is professional, clean, and detailed when working in a highly technical field. Avoid infographics and images that may confuse the applicant tracking system.

• To make your resume ATS-friendly, use appropriate subheadings throughout your document.
• Subtle touches of minimalist, dark colors on your resume like charcoal grey, navy blue, and dark maroon will help add visual contrast and uniqueness.
• Remember, shorter resumes usually perform better in online applications. Limit your resume to one to two pages. 
• Recruiters only spend an average of 7 seconds looking at a resume. Therefore, a cluttered resume with overwhelming blocks of text will put off potential employers.
• Columns are of great way to trim excess length and pare down to a one-page resume.
• Finally, use formal fonts in sizes 11 to 13 for enhanced readability.

Photo

Incorporating photos on your resume is discouraged for the risk of violating anti-discrimination and labor laws.

Sections of a Data Scientist Resume

Your resume should first highlight the technical skills that are vital to performing your job. Crucial resume sections include:

• Contact information
• Education
• Technical skills
• Work history

Depending on the format you choose, some of these sections may be combined into one. For example, if using a functional format, you could include your work history in your skills summary rather than listing it separately.

You may also choose to include additional information that may heighten your value as an employee. For example, you could add:

• Objective statement
• Soft skills
• Languages

Soft skills, such as communication or problem-solving under pressure, should not constitute a separate section but should be integrated into your skills summary.

If you speak a language other than English, this could increase your value in certain environments, for example, if working in a multilingual environment or analyzing data from a foreign source. Be sure to list your language proficiencies if applicable.

Data Scientist Resume Section Headings

When writing your data scientist resume, it’s important to include the key sections that will provide a clear view of the value you can bring to a prospective employer, including:

• Contact information
• Summary statement
• Work experience
• Technical skills
• Education
• Certifications and licenses

The order may differ depending on the resume format you choose.

• In a functional resume, you would add more detail to a skills section in place of a dedicated work history.
• In a combination resume, you might add a hyper-focused resume summary section, followed by a skills section, your work history, and your education and certifications.

You may also choose to include additional information that may solidify your expertise in the industry. For example, you could add:

• Projects
• Programming languages
• Other languages

Skills

Carefully analyze the job listing to determine what skills are most vital to the job. List these first and highlight each with an example of a time when you leveraged the skill to produce a result. If you can provide numerical data to support your claims, such as KPIs, even better.

Show your skills in IT as well as communication abilities and task management. Think about what you do better than anyone else! How is your approach to solving problems different or more effective?

Value-packed statements like these resonate with readers more than generic objective or goal statements.

Education

Nearly all data scientists will have a bachelor’s degree or master’s degree. However, relevant courses and certifications also hold weight in this industry.

In your education section, make sure to include the following details:

• Degree Title
• College + Location
• Dates of Study
• Additional training or certifications
• Professional affiliations

Work experience

To make your work experience stand out, use specific power words, numbers, and statistics to show the problems you have helped solve throughout your career.

Write short 3-5 short bullet points for each position. Include the general job duties as well as quantifiable results that demonstrate proficiency and value.

For example, did you convince senior leadership to adopt new technologies? Were you able to optimize or automate a workflow? How did your statistical analysis skills predict, resolve, or improve a process? Describe the results of your efforts!

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