Data Scientist Resume Examples and Templates for 2024

Data Scientist Resume Examples and Templates (Downloadable)

• Entry level
• Midlevel
• Senior level

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As a data scientist, your resume must show you can manage complex business challenges by uncovering patterns and finding results for issues that may otherwise go unsolved. The examples and expert tips in this guide will help you craft a resume highlighting how you use statistics, mathematics, and computer science to extract meaningful business insights.

Key takeaways:

• Highlight domain knowledge: If you’re applying for a job in an industry you’ve already worked in, make this known immediately in your profile. Hiring managers want to know if you’re already familiar with their market, products, and services.
• Quantify your impact: Apply your number-crunching skills to your own career by using data and performance metrics to describe your past accomplishments. You can easily quantify success in efficiency improvements, cost savings, and user engagement.
• Include relevant keywords: To make it through Applicant Tracking Systems (ATS), incorporate industry-specific terms, technical skills, and tools mentioned in the job description. This ensures your resume passes the test and makes it to the hiring manager.

How To Write a Data Scientist Resume Example

Organizations of all types and sizes are beginning to understand the importance of leveraging their data for competitive advantage. To effectively communicate your unique expertise and value, ensure your data scientist resume includes the following sections:

• Contact information
• Profile
• Key skills
• Professional experience
• Education and certifications

1. Share your contact information

Your name and contact information should appear at the top of your resume. Include your address, email, phone number, and a website or LinkedIn profile if you’ve developed an online presence for your career.

2. Create a profile by summarizing your data science qualifications

A hiring manager will first read your profile to gain a big-picture understanding of your professional experience, skills, and the value you can bring to an organization. Write a succinct, dynamic resume summary that begins with your job title, years of experience, and three to four specializations that align with the job posting.

In the following sentences, illustrate examples of your success. For instance, if you’ve developed machine learning (ML) models that increased revenue or streamlined processes, include that in your profile. Or, share how you work collaboratively to communicate complex technical problems in plain language. Hiring managers want to know you can bridge the gap between data insights and business strategy.

3. Create a powerful list of your data science experience

To present your work history as noteworthy accomplishments, create bullet points using action verbs and numbers. Start with your latest job title and work backward, listing the employer name, location, and employment dates. Then outline the scope and success you had in each position, demonstrating how your technical skills and knack for numbers made an impact.

While your tech savviness is important, hiring managers are interested in the full stack of your experience. Beyond the numbers, you’re a solution-finder and business strategist. So, describe how your predictive models directly impacted your past employer’s bottom line or the way you worked with stakeholders to define problems and scope projects. Show that you’re not just an algorithm whiz but you know how to apply these skills in a business context.

Resume writer’s tip: Quantify your experience

As they say, “In God we trust; all others must bring data.” Well, it’s time to apply that philosophy to your own resume by quantifying your experience. Your responsibilities and career achievements should be clear, measurable, and impactful. Employers want to know that if they hire you, they’ll receive a significant return on their investment.

Here’s how this can work for your data scientist resume:

Resume writer’s tip: Tailor your resume for each application

Just as you wouldn’t use the same model for predicting stock prices as you would for classifying images, avoid sending out one version of your resume for every application. Each employer will have their own job requirements and needs, so some of your experiences will be more relevant than others.

Say you’re applying for a role heavy on natural language processing (NLP). In that case, you would bring your language processing projects to the forefront. For a position focused on financial modeling, emphasize your experience with time series analysis and predictive analytics. Use the job description as your guide to determine which qualifications and experiences to prioritize.

What if you don’t have experience?

If you’re an aspiring data scientist with more enthusiasm than experience, you can still create an effective resume. Data science positions require a baseline knowledge of statistics, programming, and data analysis which you’ve likely gained through studies and coursework. Since you haven’t held an official job yet, you’ll need to demonstrate this competency creatively.

You’ve likely been spending hours building your GitHub gallery, participating in internships, or competing in hackathons and Kaggle competitions. Highlight these experiences on your resume and list the relevant skills you’ve developed and projects you’ve completed as a result.

4. List any education and certifications relevant to data science

It’s typical for employers to require at least a bachelor’s degree in data science, computer science, statistics, or applied mathematics. Many even prefer an advanced degree. List your academic history in reverse chronological order, starting with your highest level of education first.

If you’ve spent any time in the tech industry, you know how fast software and frameworks can change. Show you’re committed to staying on top of the latest technologies by listing your data science certifications. Depending on your area of focus, certifications in cloud computing, ML, big data, and programming can help you stand out from other applicants.

5. List key skills and proficiencies for data science

Data science roles require a wide range of skills, from analytical thinking to collaborating cross-functionally with colleagues who may not be as tech-savvy. In the key skills section of your resume, list the hard skills required to perform your job and the soft skills that present you as a team member. The list below will give you a good idea of what you can include in your data scientist resume:

Resume writer’s tip: Use strong action verbs

To have the biggest impact in the professional experience section, use action verbs at the beginning of each bullet point. Your job descriptions need to pack a punch without taking up your entire resume, and these dynamic words help compress complex data science projects into concise accomplishments. These action verbs below will help you get started:

How To Pick the Best Data Scientist Resume Template

Remember, content is king. Hiring managers can get distracted by busy graphics and fonts, so keep it simple. Plus, your template should reflect the type of position you’re seeking and the company’s culture. For instance, if you’re applying for a data scientist position at a buttoned-up organization, you would select a more traditional, professional template than one for an edgy startup. The key is to tailor your resume to the audience in both content and aesthetic.

Data Scientist Text-Only Resume Examples and Templates

Frequently Asked Questions: Data Scientist Resume Examples and Advice

How do you align your resume with a data scientist job description?

Data science is expected to see explosive growth over the next decade, with the Bureau of Labor Statistics projecting a 39% increase between 2022 and 2032. Opportunities will abound with an increase in demand, but you’ll still need an optimized resume to land your dream data science job.

Start by identifying key terms and phrases in the job description and match them up with your own experiences. Then, naturally incorporate this language throughout your resume to demonstrate you meet the job’s requirements.

For instance, if the job ad repeatedly mentions “big data analytics” and “Spark,” feature these terms on your resume, backed with concrete examples of your own experience with them. This isn’t about copying data — you’re simply optimizing your own qualifications to increase your probability of passing through ATS filters and management review.

What is the best data scientist resume format?

In data science, technical skills and knowledge are vital, but hands-on experience is what’s often most important to hiring managers. They want to see a proven track record of successfully applying all those algorithms and methodologies in real-world scenarios.

Knowing that, the reverse chronological format is the best choice. This clear and informative layout shows career trends over time, demonstrating how you’ve excelled and leveled up your data science skills year-over-year.

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Andrew Stoner

Executive Resume Writer and Career Coach

Andrew Stoner is an executive career coach and resume writer with 17 years of experience as a hiring manager and operations leader at two Fortune 500 Financial Services companies, and as the career services director at two major university business schools.