The Keys to Writing a Successful Data Scientist Resume
The demand for data scientists is growing rapidly in just about every corner of the world. With the increasing number of professionals looking to break into the field, well-trained and aspiring data scientists alike will have to prove to potential employers that they can handle the everyday tasks that come with the role. Some of the most favorable skills include machine learning, data analysis, and Python. While learning those skills should be the first priority, it’s also important to know how to translate your knowledge into a selling point for prospective employers.
Fortunately, job seekers don’t have to reinvent the wheel to showcase their data science proficiency. A good old-fashioned resume and an impressive portfolio are usually enough to do the trick. If you’re not particularly excited about drafting a new set of application documents, it may help to reframe it as a fun personal development exercise. You can even incorporate your data science skills into the process, highlighting your achievement stats in a unique way.
Last Updated March 2023
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Let’s go through the sections of a resume and see what one for a data science career looks like before we look at an example.
Resume sections and components
The header information should be at the top of every page. On the first page, your name and contact information should be prominent. If your resume is on a desk with lots of other papers, this is what they will see and catch their eye.
At a minimum, include your legal name, email address, and phone number. You’ll likely do most of your communications online, so there is no reason to disclose your physical address.
Who are you, and what are your goals? Try to keep this section down to a 30-second read and use industry keywords.
In our resume sample below, we start with a title. It is clear that the candidate is a data scientist. In the summary text, there are two parts. The first states a career goal — what kind of opportunities are you looking for? Use the summary to personalize your resume and provide some insights about you. What makes you unique? Where do you see your career headed? How would your colleagues describe you? Feel free to include something about your values and what you are passionate about.
A data scientist’s resume should be clear on the candidate’s skills, experiences, and career goals. Some careers focus on business intelligence, while others are technical and work on the data science side of the house. Advanced candidates with technical skills and in-depth business knowledge will combine both business intelligence and data science skills. While writing the body of your resume, showcase your best skills to get the job you want. Use this section to tell the story of where you’ve been, and focus on communicating your current skills linked to your goals for future projects and tasks.
No matter how many years of experience you have, try to keep the resume under four pages. If you are new to the working world, use personal or school projects to show your experience.
After the summary, include a list of relevant business and technical skills that would be useful in a data science role. A table format with two or three columns will give this section a bit of punch. Decide how to group and order them. Use keywords. Separate the business from the technical. If the order is not chronological, the reader will assume the order reflects the candidate’s preference.
A data scientist collects, analyzes, and interprets data. Goals are often improving operations or helping the business gain a competitive edge over rivals. Your experience should be accomplishment-driven and quantified.
Order your experience chronologically, with the latest on top. Include information about the company and your contributions:
- Company name and location
- Title, role, or position
- Start and end dates
- A brief description of the team or department goals
- What were your specific contributions, and how did they advance the goals of the team or department? Did you save money, reduce errors or increase sales? If you managed a team, describe the size and locations.
The Projects section is a fairly new addition to resumes. It can be useful for many science, technology, engineering, and mathematics candidates. If you don’t have any professional experience or want to emphasize a personal interest, the Projects section can help. Candidates changing a career later in life can use Projects to show an interest and newly developed skills.
If the Project work is chronologically the last item in your experience history, consider placing it above the Professional Experience section.
Projects are often personal and show a candidate’s passion for a particular technology or line of business. If you include a Project section, use it like the Summary section to continue your personal story.
Credentials and skills
The end of your resume dots the i’s and crosses the t’s. While the summary and body sections tell a story about your career goals and achievements, the education and skills sections provide a deep dive into the tools in your toolkit.
Like the professional section, the education section should list your accomplishments chronologically, with the latest at the top. Include traditional college and university experiences, certificates, and online courses.
Include notable accomplishments like graduating with honors.
There is some information that can be revealing in a way that the candidate may choose to obscure. This includes your GPA and the date of graduation, and they are optional.
Technical skills and languages
Using the same columnar list format used in the Expertise section, list your skills with programming languages and tools. Group and order them in a way that helps the reader find the skills they are looking for. Consider using bolded titles to group items.
If you are multi-lingual, add this to your resume. Be clear and state your skill level with the language.
This section can be helpful if you have an interest and regularly volunteer your time with an organization that aligns with your interest. This can reveal something personal and can help to flesh out your image or character.
Whatever it is you like to do, feel free to include it. You may find a new running mate!
There are lots of titles for this section. But the point is to include your online presence. This could be anything from LinkedIn, GitHub, or a personal web page.
Let’s move on and look at an example resume. Data scientists know the importance of formatting data as an important communication tool. Take advantage of your visual display skills and create a resume that communicates your skills and goals effectively. Choose a font and use color sparingly. Using an online resume template can be a good starting point.
In real estate, people buy homes not only because they like the house but also because they enjoy the look of the neighborhood. Your resume should be unique but not so different that it doesn’t belong in the neighborhood. Look for an example format online if you have concerns about creating your own style.
Pauletta C. Danca
New York, NY 10038 | (555) 555-5555 | [email protected]
A data scientist working to better understand how neural activity motivates and shapes human behavior.
My expertise includes data analysis and interpretation and the development and implementation of research tools. I enjoy generating new ideas and developing solutions to complex problems. My colleagues would describe me as a focused, resourceful individual who maintains a positive attitude while tackling difficult problems. I am seeking opportunities to develop and promote technologies that benefit human health.
|Data and Quantitative Analysis||KPI Dashboard Reporting|
ABC Pharma Research Inc. Jan 2018 – Current
New York, New York Business Intelligence (BI) Analyst
Worked with a team to support external clients with diagnostic and predictive analysis of sales and marketing campaign data. Our team worked on retail sales businesses selling home consumer goods.
- Created a standard process in Python to clean, prepare, and load client data into SQL databases
- Developed a SAS program to automate the refinement of linear regression models for specific segments of the client base. This program saves the department 25 person-hours each month.
- Launched a Client Projects Working Group (CPWG) to address operational efficiencies across client projects
- CPWG achieved a 20% increase in shared code across client projects and a 10% increase in on-time delivery with clients
Next University Research March 2016 – Dec 2017
Philadelphia, PA Big Data Analyst – Healthcare
Within the healthcare research department, my responsibilities were to create a process to maintain a database with insurance claim data from HealthData.gov.
- Worked on a team to extract, prepare and load research data from HealthData.gov to SAS models
- Developed automated Scoring Code for in-hospital data
- Created Dashboard reporting of SAS model results from scored data
Health Care Analysis
- Wanted to analyze the consistency of treatment for childhood brain cancers
- Created a database and cleaned and loaded public data for analysis
- Services Codes – loaded the Current Procedural Terminology (CPT) and the Healthcare Common Procedure Coding System (HCPCS) Level 1 codes into a database
- Diagnosis Codes – loaded the International Classification of Diseases (ICD) into the database
- Public health data from Healthdata.gov
- Performed analysis to find outliers in the consistency of services prescribed for childhood brain cancers
ABC University – B.S Mathematics, Minor in Computer Science
- Programming C++, JAVA, Python, SAS (base SAS and Macros), SQL
- Databases Hadoop, Mongo, SQL,
- Machine Learning Supervised Learning Linear and logistic regressions, decision trees, support vector machines (SVM)
- Unsupervised Learning K-means clustering, principal component analysis (PCA)
- Deep Learning and Neural Networks
- Data Visualization Microsoft Power BI, Excel, Google Sheets
- English native speaker
- French Read
- Italian Read/Write/Speaking
- Spanish Read/Write/Speaking
- LinkedIn linkedin.com/MyLinkedIn
- GitHub github.com/MyGit
- Personal Website and Blog https://MyWebsite.com
The data science field provides exciting and engaging careers. Because it’s still a relatively new concept, data scientists are continuing to make discoveries every day in new ways and places they can apply their skills. In one survey, over 90% of data scientists reported being happy with their jobs, and almost 50% used the word thrilled.
Due to a shortage of qualified candidates, new education programs are available to help aspiring data scientists master the field — and are doing an excellent job helping companies fill their vacant positions. Udemy alone has a great number of resources to get you on track for a successful data career. The data science field is still growing and learning. Being at the forefront of new technology provides exciting opportunities for on-the-job growth.
Chance favors the prepared mind. The is a quote from the famous French chemist and microbiologist Louis Pasteur. Take advantage of online courses to steer your career in the direction you want. Prepare for those on-the-job promotions and opportunities!
Accepting a position with a company on the upswing of data science can be a learning curve for you and the company. Companies may not understand what they are asking for or what the science can offer. Add to that the fact that data is still messy and dirty. The effort to transform the data into a useful format can be more than you or the company anticipates.
The goal of a resume is to introduce who you are and your skills and accomplishments. It is an informal document that reflects your career story in an easy-to-read and engaging format. Use the summary as an opening to introduce yourself. Use descriptive words to show you are focused, a team builder, or a good leader. Be clear on what you are looking for in your next position or career. Feel free to indicate whether you are open to the wild ride of a startup, a stable company, or a research firm. Convey a sense of joy and satisfaction with the Professional Work and Projects sections. Adding a bit of storytelling will keep the reader engaged and wanting to continue the conversation in an interview!
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