Complete Data Scientist Resume Guide (with FAANG Templates)

Data science is a competitive field, and creating a resume that helps you stand out can be tough.

Below, we break down the process of writing a data science resume from scratch.

Alternatively, use our data science resume templates from real candidates.

We'll help you:

• Write a resume that attracts more interviews, whether you're a junior or senior data scientist.
• Highlight the most relevant projects that align with the goals of the role you're applying for.

This guide was written by Alex Reyes, an Exponent career coach, resume coach, and senior technical recruiter with 20+ years of experience.

Key Takeaways

• A well-structured data science resume should have detailed hands-on data experience, highlighted projects and impacts, a technical skills section, and relevant education.
• Customizing your resume for each job application is essential. Tailor your summary and role descriptions to align with specific job requirements and company culture. Mapping your accomplishments to the Data Scientist core skills will increase readability and alignment for most job descriptions in the market. 
• Balancing technical skills with soft skills, such as communication and critical thinking, boosts your resume’s effectiveness.

[Template] Senior Data Scientist (Pinterest)

This template comes from a senior data scientist who landed a Pinterest role.

You can copy this template on Google Docs and use it as a foundation for your resume.

Why this resume works

1. Quantified Achievements: This resume effectively quantifies accomplishments, such as improving MDG accuracy from 96% to 98.8% and driving a 15% revenue increase at Merkle.
2. Relevant Technical Skills: The resume showcases the candidate’s proficiency in key tools like SQL, Python, and data analysis techniques.
3. Project and Tool Integration: The resume integrates projects and tools directly into the work experience descriptions, showing practical, hands-on experience.

[Template] Junior Data Scientist (Apple)

This is an actual resume example from a junior data scientist who landed a job at Apple after working as a data analyst and intern.

Copy this template on Google Docs to kickstart your resume.

Why this resume works

1. Quantified Achievements: The resume effectively quantifies contributions, such as improving model accuracy and driving a 15% revenue increase. Highlighting these metrics shows the candidate’s work's direct impact.
2. Project and Tool Integration: By integrating projects with work experience, the resume clearly demonstrates practical data analysis and model development abilities.

Key Elements

Every successful resume should include:

• Contact Information: Place your name, location, email, and links to your LinkedIn or GitHub profiles at the top. Keep this section concise—no more than two single-spaced lines.
• Professional Summary: Provide a brief overview of your key achievements and skills. Junior candidates should focus on education and relevant projects. This summary should be 3-5 lines, covering who you are, what you do, your major accomplishments, and your next career goal.
• Work Experience: Highlight your most relevant roles in reverse chronological order, focusing on measurable achievements.
• Projects: Showcase projects that demonstrate your data analysis and modeling skills. Align these projects with the industry and the products or services you’re targeting. As you gain more experience, this section may be streamlined.
• Skills Section: Highlight the technical skills and tools relevant to the job. List your most proficient skills first, as hiring managers often assume these are your strongest.
• Education: Include your degree(s) and any relevant coursework or certifications, especially if you’re a recent graduate. For experienced candidates, this section should be brief.

Formatting Tips

• Use Reverse-Chronological Order: Start with your most recent work experience.
• Keep It Concise: If you have less than five years of experience, aim to keep everything on a single page. Two pages are OK for those with more experience.
• Be ATS-Friendly: Use simple formatting and relevant keywords to ensure your resume can pass through Applicant Tracking Systems (ATS) and reach a hiring manager.
• Professional Design: Use a clean, professional format with easy-to-read fonts like Arial or Calibri.

Professional Summary

Your data science resume summary should be your personal pitch, summarizing your background and experience in 3 to 5 sentences.

Structure it like this:

• Who you are
• What you do
• What you’re known for (significant career accomplishment)
• Where you’re going next (target role - function and/or industry)

For example:

Led, developed, and launched X product into a new market, resulting in X% market adoption rate and $XXX revenue.

Focus on your most notable skills and achievements, such as successful model deployments, A/B tests, or impactful statistical analyses.

Avoid discussing personal career ambitions in this section.

Examples

Here’s an example summary from a senior data scientist:

Senior Data Scientist with 10+ years of experience in X and Y industries. Developed a machine learning algorithm that achieved 94% prediction accuracy in global public health applications. Currently seeking to join X company as their next Lead Data Scientist.

And here’s an example from a new graduate:

Recent CS graduate from CMU & Data Scientist with 2 years of experience in Python. Developed pricing models for SaaS products as an intern at a global SaaS HR Services company. Led the university statistics club, growing it to 8 campuses in 6 months.

Work Experience

Focus on your achievements rather than merely listing job duties. Align your accomplishments with the core skills required for the data scientist role you’re targeting.

A “skill: accomplishment” framework will help your resume read like a job description, aligning your skills with most job descriptions.

Showcase your direct impact on key performance indicators (KPIs) such as revenue, growth, or retention.

Examples

Here’s an example from a senior data scientist at a large video game company:

• Financial Modeling & Data Accuracy: Identified and corrected a flaw in the financial model that overlooked up to 10 million accounts, leading to accurate data capture. Optimized model run time from 3 weeks to 2 days and developed an alternative hypothesis, enhancing overall model reliability.
• Data Management & Compliance: Built an audit table that accurately stores financial data linked to the latest Geo-IP history, ensuring precise financial reporting and compliance.
• Automation & Reporting: Created automated reports in Tableau, Excel, and FSG, streamlining processes for Tax and Accounting. Translated Hive SQL and Unix Shell Scripts into Python scripts in Databricks, automating workflows and improving efficiency.
• Process Improvement & Efficiency: Developed Python scripts that reduced manual work by 500 hours annually, significantly increasing productivity and freeing up resources for higher-value tasks.
• Data Analysis & Strategic Insight: Conducted ad-hoc analyses to identify year-over-year trends and explain sales behavior, providing actionable insights that informed strategic decision-making.

Here’s an example from a mid-level data scientist at AT&T:

• Propensity Modeling & Customer Targeting: Developed and managed propensity models, improving customer targeting accuracy by 22%, leading to a 15% increase in campaign conversion rates.
• Predictive Modeling & Churn Prediction: Implemented speech-to-text models that predicted churn with 85% accuracy across mobile and internet services.
• A/B Testing & Customer Engagement: Designed and executed A/B tests that personalized customer interactions, uplifting customer engagement, and increased promotional campaign success rates.
• Team Leadership & Project Efficiency: Guided team members and interns, ensuring accurate query logic and model development, contributing to a 25% reduction in project turnaround time.

Past Projects

Highlight projects that demonstrate your technical skills and problem-solving abilities.

Include details like the tools and languages used (e.g., Python, R, TensorFlow) and the outcomes of your work. As you gain more experience, you can streamline this section.

For example, link to a GitHub repository of an open-source project you contributed to.

Technical Skills

Tailor your skills list to the job description, focusing on the languages, frameworks, and workflows that are most relevant. It’s better to highlight fewer skills you are genuinely proficient in than to overstate your abilities.

Common technical skills to include on a data science resume:

• Python Data Analysis Frameworks (NumPy, Pandas, Scikit-Learn, Keras)
• SQL, MySQL
• Data Visualization (Tableau, Excel)
• Machine Learning Techniques (supervised and unsupervised learning)
• productionizing Models

Additionally, map your technical skills to your work experience where applicable so the reader can see how you’ve applied these skills in real-world scenarios.

This also improves searchability on LinkedIn and in applicant tracking systems.

• Propensity Modeling & Customer Targeting: Leveraged Python Data Analysis Frameworks (NumPy, Pandas, Scikit-Learn) to develop and manage propensity models, improving customer targeting accuracy by 22%. Applied supervised learning techniques to enhance model precision, leading to a 15% increase in campaign conversion rates.
• Predictive Modeling & Churn Prediction: Implemented speech-to-text models using Python and integrated Scikit-Learn for predictive analysis, achieving 85% accuracy in churn prediction across mobile and internet services. Productionized these models, ensuring they were seamlessly deployed into the business pipeline.
• A/B Testing & Customer Engagement: Designed and executed A/B tests using SQL and MySQL to analyze customer data and personalize interactions. This approach, combined with machine learning techniques for customer segmentation, increased customer engagement and improved promotional campaign success rates. Utilized Tableau and Excel for clear and compelling data visualization, facilitating decision-making.
• Team Leadership & Project Efficiency: Guided team members and interns using SQL and Python to ensure accurate query logic and model development. Mentored the team in best practices for deploying machine learning models, contributing to a 25% reduction in project turnaround time.

If you’re still building your experience, focus on the proactive steps you’ve taken to develop your skills—such as boot camps, courses, or mentorship programs.

Soft Skills

Soft skills are just as important as technical skills in data science. You’ll be expected to work cross-functionally and clearly explain your findings to product managers, data engineers, software engineers, and business leaders. Articulating how you collaborate with others can set you apart, especially for mid-career to senior-career level roles.

Highlight key soft skills in your work experience:

• Communication: Ability to convey complex information clearly to non-technical stakeholders.
• Critical Thinking: Ability to analyze data objectively and challenge assumptions.
• Creativity: Ability to approach problems with unique solutions, often filling in the blanks of missing data.

Here’s an example:

Stakeholder Communication: Primary point of contact for the senior leadership team; presented performance results, development milestones, and new use cases throughout the development cycle.

Education and Certifications

Education and certifications are particularly important in data science. List your degree(s), relevant coursework, and data science or machine learning certifications.

You can include details like relevant projects or internships if you're a recent graduate. For those transitioning from other fields, consider condensing the education segment to emphasize the qualifications most applicable to data science.

Resume Customization

Customizing your resume for each job application is essential. By tailoring your resume to reflect the nuances of the position and the company’s culture, you demonstrate that you’ve thoroughly considered your suitability for the role.

Rework your resume summary to align your skills, experience, and qualifications with the job you’re applying for. Mapping key skills, technologies, and qualifications across your summary, work experience, and skills section will help create a cohesive narrative highlighting your value to the organization.

Here’s how to structure your summary in 3-5 lines:

• Who you are
• What you do
• What you’re known for (significant career accomplishments)
  • Example: Led, developed, and launched X product into a new target market, resulting in X% market adoption rate and $XXX revenue.
• Where you’re going next (target role - function and/or industry)

Example:

Data Scientist with a strong track record of leveraging advanced statistical techniques and machine learning to drive strategic decision-making and business outcomes at scale. At Pinterest, I spearheaded data initiatives that enhanced product marketing, reducing user churn by 21%. I seek to apply my expertise in a forward-thinking organization, aiming to lead data-driven innovations in the tech industry.

Customize your past employment descriptions by emphasizing projects or tasks directly relevant to your target role. For example, if expertise with ETL tools is required, prominently feature any experience developing ETL pipelines. Use the Task-Action-Result framework to build out your resume bullets.

Additional Sections

Enhance your resume by including sections like:

• Publications: Highlight your research contributions.
• Presentations: Show your ability to communicate complex topics.
• Awards: Demonstrate industry recognition.
• Professional Affiliations: Indicate active involvement in the data science community.

Including these sections can help demonstrate your expertise and commitment to the field.

Don’t overlook a section on blog posts. These entries can reflect your active interest and ongoing dialogue about current trends or discussions relevant to data science. Optimize these segments based on their alignment with the target job requirements and your vocational experiences.

FAQs

How long should a data science resume be?

The length of your resume depends on your work experience. A data science resume should typically be one page for up to 5 years of experience. If you have 10+ years of experience, you can expand your resume to two pages. Even experienced professionals should focus on the most relevant details.

What are the key elements of a data science resume?

A data science resume should feature your contact information, a summary, relevant work experience, projects, key skills, and educational background.

How should I format my data science resume?

Maintain a clean and professional design. A single column is preferred. Use easy-to-read fonts, incorporate sufficient white space for clarity, and use bullet points for better legibility.

Why is it important to customize my resume for each application?

Tailoring your resume to each job shows that you’ve focused on the role’s nuances and demonstrates your commitment, increasing your chances of capturing the hiring manager’s attention.

What additional sections can enhance my data science resume?

Enhance your resume with sections for publications, presentations, awards, professional affiliations, and blog posts. These additions can showcase your research, communication skills, and active participation in the industry, reinforcing your expertise and commitment to the field.