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Data Scientist Resume: How To Show Off Your Analytical Skills

Predictive analytics, linear regressions, and data analysis may come easy to you. But, what about writing a hiring-manager-approved data scientist resume? Let us help you with that.

An effective data scientist resume consists of relevant experience, great design, and data-supported highlights. We’ll provide valuable writing tips, resume sections to include, and formatting guidelines.

Over 100,000 people have followed our guidance to write a professional-looking resume in a fraction of the time. Let’s show hiring managers that you’re the right data scientist for the job.

What Makes a Great Data Scientist Resume?

A data scientist resume must show hiring managers you can be the foundational support of their company’s decision making. You must explain that you can be trusted to provide valuable data-backed insights for all functions of the company — from marketing to product development to forecasting.

Hiring managers are seeking candidates who are well-versed in all things data and specialize in the right analytical and programming skills to match the job description. They want to hire someone who has the quantitative and qualitative skills needed to perform tasks like manipulating data sets, data mining, and data modeling.

They also want a well-rounded data scientist who can communicate their findings to clients and internal stakeholders. This includes being experienced in data visualization, presentation skills, and client management.

Most importantly, your resume is a first-hand look into your thought process. Support your career highlights with data to prove you’re the analytical mind they’re seeking. We’ve covered what hiring managers want to see on your resume. Now it’s time to fill in each resume section.

Data Science Resume Sections

Your resume needs to include the following sections: contact information, resume summary, work experience, technical skills, and education. Here’s the information you need to provide in each section.

Contact Information

This section is relatively straightforward — so keep it simple. Stick to the main pieces of information hiring managers can use to contact you.

Stay away from including excessive personal information like your picture, gender, or nationality. You don’t need to list your entire physical address — your city and state are sufficient.

This is also where you would list your portfolio or a link to your GitHub account if you have one. This is an opportunity to allow the hiring manager to see your work in action with examples of data models, or SQL and Python code you’ve written.

What to include:

  • Name

  • Title

  • City and state

  • Email

  • Phone number

  • Portfolio or GitHub account

Example:

  • Tim Jones | Data Scientist | Los Angeles, California

  • Tjones@gmail.com | 888-431-2368 | github.com/Tjones

Resume Summary

Your resume summary is the first section hiring managers will read. It needs to be a compelling overview of your entire data science career and explain why you’re the right fit for the job.

The resume summary should explain your specialties and how you’re different from all other candidates. For example, some data scientists specialize in manipulating and cleaning data with Python while others build machine learning models. Decide what makes your major strengths are and communicate this in your resume summary.

Each sentence should be carefully crafted and serve a purpose. The hiring manager should be able to read your summary and immediately see the value you can bring to their company.

We recommend writing your resume summary after you’ve gone through and listed all of your work experience. This will enable you to pick your biggest accomplishments and insert them into your resume summary.

Example:

  • Data scientist with over five years of experience designing and implementing machine learning algorithms. Extensive experience creating deep learning architectures that accurately predicted customer behavior for multiple SaaS products.

Work Experience

Your work experience section starts getting into the finer details of your previous roles at other companies. It will cover all of your accomplishments and the impact you had on data science teams.

Think of each bullet point as an insight you’d deliver to a client or internal team member. They should include the problem, solution, and data-backed results from your work on data science projects. This is an opportunity to show hiring managers that your thought process is rooted in data.

It’s important to use relevant experience tailored to the job description for each job you’re applying to. This may take more time and effort, but a hiring manager will be more likely to set up an interview if your experience directly relates to what they’re looking for.

What to include:

  • Job title

  • Company

  • Dates employed

  • 3-4 bullet points highlighting your contributions

Example:

SaaS Company | Data Engineer | April 2018 – May 2020

  • Leveraged cluster analyses of customer behavioral data to segment marketing communications

  • Created a machine-learning algorithm that predicted customer churn rate with a 20% improved accuracy rate

  • Implemented automated Tableau dashboards with SQL to improve data usability across all functions of the company

Technical Skills

The ideal data scientist possesses a unique set of statistical analysis, programming, and visualization skills to solve complex business problems. One skill alone won’t earn a job offer — but, the combination of multiple technical skills will.

Familiarity with programming languages that manipulate data is a prerequisite for most data science roles. The ability to use data management and visualization tools is also a major bonus.

The technical skills section should focus on skills that relate to the job you’re applying for. You can leave off irrelevant skills that won’t be used at that particular job.

Example:

  • Python

  • SQL

  • R

  • BigQuery

  • Hive

  • AWS

  • Machine learning

  • Data mining

  • Data visualization

  • Hadoop

  • Tableau

  • Decision tree analysis

  • Spark

  • Data modeling

  • Data processing

Education

Your education section will show recruiters you have built a foundation in data analytics long before entering the workforce. Similar to the contact section, you should keep it simple and include basic education information.

Hiring managers will be on the lookout for degrees in mathematics, statistics, or computer science. But, don’t worry if your degree isn’t in one of these areas. Your years of experience in the workforce will make up for it.

Recent graduates who have less professional experience can choose to include additional details to highlight this section. This can include adding analytical projects or organizational involvement.

What to include:

  • Name of institution

  • Degree obtained

  • Dates attended

Example:

  • Stanford University | Bachelor’s Degree in Statistics | 2010 – 2014

Resume Formatting

One aspect of data science includes data visualization and creating easy to read presentations to report your findings. Your resume can exemplify your design skills and your ability to organize information.

You want your resume to look polished and professional to stand out from other candidates. You can take a look at some of these data scientist resume samples to get a better understanding of how your resume should look.

Here are some areas for consideration:

  • Resume style: Use a reverse chronological resume format that walks through your work experience starting with your most recent role.

  • Margins: Your margins should be large enough for printers to handle, but small enough to limit the length of your resume.

  • Font: Stick to professional-looking fonts that are clean and modern. You should be thinking about readability for both print and digital.

  • Font size: Readability is also key here. Your font size needs to be big enough so hiring managers’ eyes aren’t strained while reading.

  • Spacing: Leave enough space between lines to avoid crowding your information. Once again, readability is crucial here.

  • White space: Leave enough white space between your resume sections. You want sections to be easily distinguished from one another.

  • Color: Incorporate color on your resume, but keep it simple. Stick to one or two colors that are professional looking to give your resume some character.

  • Resume length: Your resume should be one to two pages long. Data scientists with extensive experience can extend their resume to two pages if needed.

Formatting your resume is the final step before completion. Once you've polished your resume, it's time to start your job hunt and apply to data scientist job openings that match your skill set.

Building Trust With Your Resume

Resumes are all about building trust with hiring managers. They need to be able to quickly scan your resume and understand why you're the right fit for the job. After all, you'll be the person people turn to when making major business decisions throughout the company.

We've laid out everything you need to include on your resume, but we can also help expedite your resume writing process. Our resume builder has been used by over 100,000 people who have landed jobs at companies like Google, Apple, and Square. Check out our hiring manager approved resume templates to get started.

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