How To Write a Data Science Resume
In the realm of burgeoning AI and machine learning advancements, your data science resume should showcase your proficiency in this rapidly expanding technological domain. As you navigate the diverse array of opportunities within this field, a compelling resume, driven by notable achievements, becomes your primary tool for standing out amidst fierce competition. Throughout this guide, we provide valuable insights to help you translate your data science experience into a powerful marketing document.
- Entry-Level
- Mid-Career
- Senior-Level
1. Write a dynamic profile summarizing your data science qualifications
Creating a compelling snapshot of your data science career is the best way to draw the reader in. Start by listing your job title, years of experience, and three to four specializations that align with the job posting. In the subsequent sentences, you should establish yourself as a thought leader within your space.
For example, if you helped to pioneer the integration of ChatGPT and other AI solutions, you’d want to feature this information directly in your profile. If you played a key role in developing machine learning and deep learning solutions for autonomous vehicles, you should feature these achievements directly in your summary. Providing these types of insights will help you tell your story and highlight the value you can bring to prospective employers.
Senior-Level Profile Example
A senior data scientist with 10+ years of experience using machine learning, big data, and deep learning to deliver data-driven solutions for enterprise organizations. A proven track record of creating dynamic machine learning algorithms to enhance data visualization and drive positive business outcomes.
Entry-Level Profile Example
A data scientist with three years of professional experience specializing in Python, machine learning, Big Data, and data management. Adept at performing statistical analysis on large, complex data sets to drive business intelligence and enhance data visualization.
2. Outline your data science experience in a compelling list
To craft an accomplishment-driven professional experience section, you’ll want to create bullet points emphasizing your career achievements rather than job responsibilities. Companies are interested in results, and as a data scientist, you should be able to demonstrate the bottom-line value of your contributions by incorporating data, metrics, and monetary figures.
Emphasize your ability to collaborate with cross-functional teams and translate complex technical concepts into accessible language, as you won’t always work solely with data scientists on every project.
Senior-Level Professional Experience Example
Senior Data Scientist
Omicron Biotech, Buffalo, NY | October 2016 – present
- Collect, study, and interpret large datasets of research results to enhance data-driven decision-making for a $100M biotechnology company
- Develop advanced machine learning models
- Oversee a 20-person business intelligence team, manage data analytics on an enterprise scale, and ensure appropriate implementation of statistical analysis, predictive modeling, and deep learning approaches
- Communicate data using a variety of visualization approaches, including Power BI and Tableau
Entry-Level Professional Experience Example
Data Scientist
Omega Real Estate, Raleigh, NC | July 2021 – present
- Collaborated with team members to improve customer relationship management database, leading to improved customer service outcomes in a high-volume real estate firm
- Used predictive analytics, including data mining techniques, to forecast company sales with 94% accuracy
- Increased data security by updating encryption, IP security, and wireless transmission processes
3. Outline your education and data science-related certifications
In addition to your education, you should feature any relevant certifications you’ve achieved throughout your data science career. Although most employers will be more interested in your professional experience, obtaining other credentials won’t hurt your chances of landing the interview.
For instance, having a Certified Analytics Professional (CAP) credential could be useful for entry-level professionals, as the exam will help test your knowledge across a wide range of data science concepts. A certification alone won’t guarantee an interview for senior-level job seekers, but it will show prospective employers that you’re committed to continuous learning.
Education
Template
- [Degree Name]
- [School Name], [City, State Abbreviation] – [Graduation Month and Year]
Example
- Bachelor of Science (B.S.) Data Science
- Temple University, Philadelphia, PA – June 2016
Certifications
Template
- [Certification Name], [Awarding Organization] – [Completion Year]
Example
- Microsoft Certified Solutions Expert, Microsoft – 2019
- SAS Certified Big Data Professional, SAS – 2017
4. List key data science skills and proficiencies
Most organizations rely on an applicant tracking system (ATS) to identify qualified candidates for job openings. To get your resume through the initial screening process and into the hiring manager’s hands, you’ll want to directly incorporate keywords from the job description into your profile, professional experience, and skills section. Below, you’ll find a list of key terms and skills that you may encounter while applying for data scientist positions:
| Key Skills and Proficiencies | |
|---|---|
| Agile methodology | Artificial intelligence (AI) |
| Big data | Business intelligence |
| Data analysis | Data analytics |
| Data-driven decision making | Data modeling |
| Data science | Deep learning |
| Data visualization | Machine learning |
| Natural language processing (NLP) | Power BI |
| Predictive modeling | Python |
| R (programming language) | Statistics |
| SQL | Tableau |
5. Highlight your leadership and communication skills
Data science requires much more than crunching numbers. While you should emphasize your data science hard skills and experiences, it’s also important to show hiring managers your leadership and communication skills. After you analyze data, you must be able to communicate your insights to team members, business units, and clients, including those who may not have a strong knowledge of data science. In addition to communication, employers look for those who can lead diverse teams and collaborate cross-functionally.
How To Pick the Best Data Scientist Resume Template
When selecting your template, you should prioritize structure and readability over visual appeal. Heavy colors and bulky graphics may look nice, but they can distract the reader from your content. Try to find a template that organizes your content effectively and fits your brand. Hiring managers will always be more interested in your achievements and qualifications than the style of your template.
Data Scientist Text-Only Resume Templates and Examples
- Entry-Level
- Mid-Career
- Senior-Level
Joshua Robertson
(789) 123-4560
[email protected]
2434 Third Road, San Antonio, TX 34567
Profile
An SAS certified Data Scientist with eight years of experience using predictive analytics and classical modeling techniques to provide valuable data insights for the financial industry. A proven track record of managing data analytics to support financial management, operations, and reporting for enterprise clients.
Professional Experience
Data Scientist, Financial Data Consulting Inc., San Antonio, TX
April 2016 – Present
- Deliver data science consulting services to enterprise clients within the financial sector valued at $20M-$35M, develop algorithms and analytical models using SAS, R, and Hadoop, and educate technical and non-technical audiences on findings and data trends
- Collaborate cross-functionally with data analytics, finance, and business intelligence departments to analyze complex financial data sets and improve forecasting methodologies for client businesses
- Utilize machine learning techniques to enhance financial reporting and data visualization
Data Scientist, Gamma Finance, Dallas, TX
July 2012 – March 2016
- Analyzed datasets and communicated insights to business owners to assist with data-driven decision making
- Developed dashboards and reports that communicate a story and provide visualization of data in a way that can be best utilized by internal customers
- Evaluated business processes and recommend data science solutions to improve efficiency
Education
Master in Data Science and Analytics
University of Oklahoma, Norman, OK, September 2011 – June 2012
Bachelor of Science of Information Technology
University of Tulsa, Tulsa, OK, September 2007 – June 2011
Key Skills
- Data Visualization
- Machine Learning
- SQL
- Hadoop
- Risk Analysis
- Software Engineering
Certifications
- Senior Data Scientist, Data Science Council of America, 2018
- SAS Certification, 2019
Frequently Asked Questions: Data Science Resume Examples and Advice
What are common action verbs for data science resumes?-
Action verbs help hiring managers visualize your contributions, but it’s easy to find yourself running short on action verbs during the resume-building process. Differentiating your word choice can enhance the quality of your bullet points and eliminate the appearance of redundancy.
Try these action verbs you can use to craft your professional experience section:
| Action Verbs | |
|---|---|
| Analyzed | Built |
| Conducted | Collaborated |
| Created | Designed |
| Developed | Diagnosed |
| Drove | Enhanced |
| Evaluated | Executed |
| Identified | Implemented |
| Improved | Integrated |
| Led | Managed |
| Performed | Supported |
How do you align your resume with a data scientist job description?-
According to the Bureau of Labor Statistics, jobs for data scientists (computer and information research scientists) are projected to grow by 21% from 2021 to 2031, faster than average for most occupations. This growth is primarily driven by the advancement of AI technologies and machine learning, which have become increasingly important for businesses.
Although these projections are highly optimistic, you must align your resume with the job description to secure interviews for the most lucrative opportunities. Data science is a highly competitive field, and tailoring your document to individual job postings will significantly increase your chances of landing your next job opportunity.
For example, suppose a company is looking for an expert in machine learning. In that case, you’d want to highlight further your knowledge of deep learning, supervised learning, and unsupervised learning to substantiate yourself as a thought leader in your space.
What is the best data science resume format?-
Reverse chronological format is ideal for data science resumes. This approach ensures that your most recent and relevant experience is featured at the top of your document. It’s best to avoid functional resume formats even at the entry level. If you lack hands-on experience, you’d still be much better served by illustrating your academic projects than only listing technical skills.
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Providing a matching cover letter is a great way to help your application stand out in the open market. The cover letter allows you to tell prospective employers more about who you are as a professional and the value you can bring to their organization. In the middle paragraphs, mention something about the company’s reputation or mission statement and why this draws you to apply for the position. For more insights, view our data scientist cover letter guide.
