Your data science expertise is so profound that people often say you could predict rainfall in a desert.
But when you have to present yourself to potential employers? That’s less up your alley.
While you know that a strong resume will help showcase your skills, you may want some guidance on how to write one. Well, you’ve found a helping hand. Soon you’ll know how to craft a resume that outshines the competition and gets you callbacks.
This guide will show you:
- Data scientist resume examples at different career stages.
- What makes a great data scientist resume example.
- How to put together a job-winning resume.
- What to include in each section of your data scientist resume.
- How to select the ideal resume format and design for the position.
If you’re ready to make your resume today, head directly to our AI Resume Builder. There, you can choose a design of your liking and get advice and easy-to-edit, auto-generated text for every section.
Data scientist resume examples
What makes this a great data scientist resume sample
- Resume objective showcases adaptable skills
This candidate is adapting their experiences in various settings for a data scientist position, and they emphasize the skills that will facilitate this shift.
- Education shows credibility
With a master’s degree in their pocket, this candidate conveys to employers that they have the knowledge and skills necessary for success as a data scientist.
- Technical skills are well represented
Every detail here reinforces the fact that this applicant has extensive technical skills, perfect for complex tasks such as data analysis and software development.
What makes this a great data scientist resume sample
- Highlights their professional background
This data scientist exhibits extensive expertise and appropriately incorporates a comprehensive, detailed work history section in a chronological resume format.
- Demonstrates data analysis with metrics
Their experience is further enhanced by incorporating quantitative measures that prove they achieved concrete results in their previous role.
- Showcases a diverse skill set
Data scientists utilize both technical and interpersonal skills, and this resume effectively covers a wide range of abilities and competencies.
Looking for a specific job title? Check out more resume examples related to your career field:
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Choose the perfect format for your data scientist resume
As a data scientist, your resume is a platform for showing your numerical prowess, strategic insight and career growth.
The resume format you choose will play a major role in this narrative, and there are three main options, each tailored to a specific stage of the data science journey.
- Reverse-chronological format: For the seasoned data scientist, the chronological format highlights your career trajectory and achievements. Suppose you’re a data scientist with a long-standing involvement in the banking sector. This format puts work history in the spotlight, so hiring managers can see the sequence and consistency of your roles.
- Skills-based format: If you’re transitioning into a new industry as a data scientist, the skills-based format can be your ally. Imagine transitioning from energy to the e-commerce sector. This format puts skills on center stage, helping you highlight the universal data science skills you possess, like predictive modeling or machine learning, and your flair for uncovering insights in any industry.
- Hybrid format: If you’re a data scientist somewhere in the middle of your career, you may want to showcase a balance of your skills and experience using a hybrid format. For example, for a data scientist with some experience who is hoping to land a higher-up position, this format allows you to emphasize your project milestones and leadership potential.
Having selected the most fitting format, it’s time to inject your resume with a visually appealing yet professional design.
Find an appropriate template
As a data scientist, feel free to pick any resume template that looks good.
But all professionals should follow some common rules and guiding principles.
One key rule is picking a style that suits your job field. For example, choose a straightforward or professional template if you’re a data scientist in a formal sector like banking or health care.
But if you’re in a more relaxed or innovative field like tech startups, consider a more creative resume to show your unique thinking.
Keep in mind — you can always use our Word resume templates or go for a Google Doc template if that’s what you need.
Now, let’s start making a resume showing off your data science skills and achievements.
How to write a data scientist resume in 5 easy steps
The path toward getting your next job is having a great resume — and you can make an excellent data scientist resume in just five steps.
See our resume examples for additional inspiration.
Step 1
Look closely at the data scientist job description
Job advertisements are a valuable tool, especially for data scientists.
By scrutinizing these ads, you can pinpoint crucial technical skills and programming languages — such as Python, R, SQL or machine learning frameworks — that are in demand and continuously evolving in the field of data science.
Not only that, you can see exactly what specific companies expect you to know and tailor your resume accordingly.
So, why is this crucial? Many companies use Applicant Tracking Systems (ATS) to filter out candidates who satisfy certain criteria. These criteria typically involve a set of keywords related to skills and qualifications.
By incorporating the relevant keywords found in the job ad into your resume — like “predictive analytics,” “data mining,” “big data” or specific programming languages — you’ll successfully navigate the ATS, bringing you a step closer to your resume being reviewed by a real person.
Now, it’s time to start writing.
Step 2
Write a compelling professional summary or objective
Kick off your resume with an engaging resume profile. Resume profiles come in two distinct types — professional summaries and career objectives — and both can be very effective for data scientists.
Professional summaries are compact overviews of your career and are well-suited to those with a solid and consistent work history. They work great for data scientists who have a series of significant quantifiable achievements to share, such as improving model accuracy by a specific percentage or handling large data sets effectively.
Career objectives, on the other hand, outline your career goals and aspirations. They are particularly useful for data scientists who are transitioning across different domains or industries and entry-level applicants, as they provide an opportunity to highlight transferable skills and the unique value you can bring to a new organization.
Let’s take a look at how each of these would look on a data scientist’s resume.
Data scientist professional summary example:
Experienced data scientist with a decade-long track record in the healthcare industry. Skilled in leveraging machine learning and predictive analytics to facilitate better decision-making, contributing to a significant 20% enhancement in patient care quality. Known for developing and executing effective data strategies, resulting in a notable 30% reduction in data processing times and boosting operational efficiency.
Data scientist resume objective example:
Passionate data scientist with over eight years of experience in the energy sector aiming to transition to a fast-paced e-commerce environment. Proven success in optimizing data-driven processes, enhancing team productivity and creating strategic growth plans. Eager to apply data analysis expertise and innovative approach to a new industry, with the goal of propelling the company toward data-driven advancements and superior customer experience.
Step 3
Highlight your most relevant data scientist skills
Data scientists have so many abilities — you just need to sit down and list them.
Your skills section should showcase a blend of technical and interpersonal abilities.
Hard skills demonstrate your strength in areas like programming languages and data analysis, while interpersonal or soft skills highlight your capability to communicate effectively and work within a team. In a rapidly evolving field like data science, staying current with your technical skills is also particularly important.
Let’s look at examples of skills that data scientists often include on their resumes.
Hard and soft skills for a data scientist resume
- Programming
- Statistics
- Data visualization
- Structured Query Language (SQL)
- Machine learning
- Mathematics
- Problem-solving
- Quick learner
- Ethics
- Collaboration
- Business knowledge
- Critical thinking
Here is an example of a strong data scientist skills section:
- Familiar with Big Data platforms and tools: Hadoop, Spark and Hive.
- Expertise in machine learning and deep learning: regression, classification, clustering and neural networks.
- Exceptional communication, collaboration and leadership skills.
- Proficient in statistical analysis and mathematical modeling.
- Competent in using AI and machine learning platforms such as TensorFlow and Keras.
- Excellent understanding of databases: MySQL, MongoDB and PostgreSQL.
Make sure to cite your interpersonal skills. Employers love well-rounded candidates who have a mix of technical proficiency and excellent people skills.
Step 4
Write an excellent data scientist work experience section
While the precise duties and expectations in each data scientist role might vary, the central objective of your resume stays consistent: you want to highlight your value.
The best way to showcase your previous achievements is through a detailed work experience section. List your roles, starting with the most recent, with bullet points for each that paint a picture of your responsibilities and achievements.
Your analytical prowess and problem-solving abilities should shine through as a data scientist.
Make sure to highlight any cross-functional collaborations or projects. This underlines your ability to work within diverse teams and contexts, an important aspect of modern data science.
One last thing — use action verbs and power words. These verbs can make your experiences come alive and make even older projects feel timely and relevant.
Here’s an example of a great data scientist work history entry:
Data Scientist, TZO Digital
San Jose, CA
January 2018 – Current
- Managed and processed large data sets (over 10 TB) using SQL and Hadoop, enhancing data accessibility and utilization.
- Directed a team of over 100 employees, fostering a culture of innovation.
- Architected machine learning models in Python, leading to a 20% improvement in prediction accuracy for client marketing campaigns.
- Collaborated cross-functionally with marketing and sales teams to apply data insights, resulting in a 15% increase in sales efficiency.
- Kept pace with emerging data technologies and tools, leading to an early adoption of Apache Spark for improved data processing speeds.
As shown in the example above, quantifiable metrics can put a specific value on your contributions. When you’re aiming for a role as impactful as a data scientist, numbers speak volumes about your capabilities.
Step 5
List your relevant education and training on your data scientist resume
In today’s tech-driven landscape, even when you can learn many skills on YouTube, a data scientist’s education and professional credentials significantly influence their hireability.
The university’s alumni network alone can foster connections and stimulate discussions with others in the field.
Showcase your educational background by adding education to your resume. The most desirable candidates typically hold an advanced degree in a field like Computer Science, Statistics or Mathematics. However, data scientists often come from diverse educational backgrounds, reflecting the field’s interdisciplinary nature.
Additionally, it’s worth mentioning any relevant certifications you’ve earned. Certifications, like IBM Data Science Professional Certificate or Certified Analytics Professional (CAP), showcase your dedication to continuous learning and your proficiency in specific areas of data science.
Remember, your education section isn’t merely a list of degrees or certifications. It’s a testament to your commitment to the field and your dedication to ongoing learning.
Considering the advanced level of your academic and professional qualifications, you could probably omit your high school diploma from your resume.
How to list your data scientist education:
Education
Master of Science in Data Science
Massachusetts Institute of Technology, Cambridge, MA
Thesis: “Novel Approaches to Predictive Analytics in Health Care”
Bachelor of Science in Computer Science
University of Ohio, Athens, OH
President of the University’s Data Science Club for one year
Certifications
IBM Data Science Professional Certificate: Completed a nine-course program on Coursera, focused on open-source tools and machine learning techniques.
Certified Analytics Professional (CAP): Received the certification after demonstrating an end-to-end understanding of the analytics process, from framing business and analytics problems to acquiring data, methodology, model building, deployment and model life cycle management.
Optional sections for your data scientist resume
Awards and Certifications
As you prepare your data scientist resume, consider highlighting your most remarkable achievements, including an awards section. These recognitions can illustrate your innovative work, analytical prowess or contributions to major projects. They should underscore your potential to provide value to any organization.
Similarly, spotlighting professional affiliations or memberships can give you an edge in a competitive field. These credentials display your commitment to continually developing your expertise and knowledge in the field of data science.
How to list awards on your data scientist resume:
Data Innovation Award – September 2024
- Received recognition at the National Data Science Awards for creating an innovative machine learning model that improved operational efficiency in the health care sector.
Best Data Science Article – May 2020
- Awarded by the International Association of Data Science for a published article on the implications of deep learning in financial markets.
Projects
Tangible projects and important initiatives form the backbone of a compelling data scientist resume. These demonstrate your ability to turn data and insights into concrete results, indicating that you’re an implementer, not just a theorist.
Featuring significant projects in your data scientist resume provides insight into your analytical skills. These skills encompass innovation, problem-solving, team collaboration, time management and the ability to deliver results. It’s particularly effective when these skills align with the requirements of your target role, emphasizing your expertise in the field.
How to list programming languages on your data scientist resume:
- Proficient in Python, R and SQL
- Familiar with Java and C++
Interests and Hobbies
Adding hobbies and interests to your data scientist resume can provide a peek into your personality and other interests, supplementing your professional accomplishments.
However, it’s crucial to maintain a balance — your chosen hobbies should reflect transferable skills and be pertinent to the data science field. For instance, participating in coding challenges can underscore your problem-solving abilities, while blogging about the latest machine-learning trends can highlight your passion and commitment to staying updated in the field.
How to list interests and hobbies on your data scientist resume:
- Participate in Kaggle competitions.
- Maintain a blog on data science trends and innovative machine learning models.
- Mentor aspiring data science students.
Add a cover letter
You might be thinking, “I’m the boss. Do I really need a cover letter?”
If you’re wondering whether you need a cover letter, the answer is — it couldn’t hurt!
While it may not always be a prerequisite, a well-crafted cover letter could potentially provide you a competitive edge over other candidates.
This document gives you a chance to highlight key skills and projects that you couldn’t fully encapsulate in your resume. It’s also the perfect platform to explain how you became a data scientist or why you want to advance in this field.
If you require guidance, browse our guide on how to write a cover letter and successful cover letters examples to grasp why certain ones are more effective than others.
Ultimately, choose a cover letter template to customize in our Cover Letter Generator to ensure that your cover letter’s formatting aligns with professional standards. Ideally, it should follow the same formatting as your resume.
Let’s wrap up with a few key points to remember.
Data scientist resume — key takeaways
Tailor your resume to the job description
Align your resume to each job description, including technical skills, tools and programming languages from the listing.
Include a mix of skills
Showcase both your hard and soft skills. From machine learning methods to teamwork, you need to have a healthy mix.
Quantify your achievements
Whenever possible, include metrics to showcase your success, such as the prediction accuracy that resulted from your work.
List education credentials
Mention your degree and field of study as well as any advanced degrees or data science-specific education.
Showcase relevant credentials
Include any data science-related certifications, such as Certified Data Scientist from IBM or Data Science MicroMasters from edX.
Feature programming languages
Detail your proficiency in key programming languages used in data science such as Python, R and SQL.
Consider adding a cover letter
Even with number-heavy occupations, your words matter. Make the case for being the best candidate with a well-written cover letter.
Questions to help you with your job search
Last Updated: January 01, 2025
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Don is a Certified Professional Resume Writer (CPRW) with more than 10 years’ experience creating digital content, including four years helping job seekers develop their careers. He holds an M.S. in Journalism from Northwestern University.
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