Writing a Data Scientist CV: What to Know

Data scientists are responsible for some of the most impactful insights organisations use to guide their decision-making. To secure such an important position, you have to make a good first impression with your CV.

Your CV should follow a reverse chronological format. Start with your work experience, followed by education, skills, and achievements, and include additional content (professional interests, publications, conferences) if you have space. Read on for more helpful tips on writing your CV as a data scientist.

What is a data scientist?

A data scientist works with data to answer questions and solve problems for a team, company, or organisation. Data scientists gather, analyse, process, and model data and interpret the results. Data can be structured (dates, credit card numbers, and names) or unstructured (social media posts, audio files, or surveillance video).

Many data scientists use computer science, social science, and maths skills to uncover trends and create solutions. They are both discoverers and problem-solvers. 

Must-have elements for a data scientist CV

Elements of a well-written data scientist CV include work experience, contact information, skills, and education. You also can sprinkle in achievements, awards, or professional interests as the room allows. Format your CV so that the focus is on those core elements.

When choosing a format, consider using reverse chronological order. This will show potential employers your most recent work history and education. Those items tend to be the most relevant information, and a reverse chronological order format makes it easy to find these details. 

1. Include your work experience.

Your experience should be the first thing an employer sees after your contact information and an objective or brief summary. It’s a good idea to jump right to experience because, in data science, experience carries a lot of weight. 

When listing your experiences, list them chronologically from your current job or most recent job. List relevant experiences only. For each position you have held, organise the following pertinent information in this order:

• Title of your position

• Name of the company

• Where the company is located

• Your start and end dates (or "present" if you currently hold the position)

• Bulleted list of your most noteworthy achievements and key responsibilities

If you’re a new graduate without experience, try to build that up before creating your CV. Some ideas include: 

• Freelancing in a relevant position or organisation 

• Contributing to open-source projects like GitHub to build your portfolio 

• Creating mock projects that you can link to show your skills 

2. List your education. 

List your education on your CV, with the most advanced degree listed first. If you don’t have a relevant degree in data science, list your A Level or equivalent education. List education after experience since experience takes priority. However, you can list education first if you are a recent graduate without any experience yet or have limited experience. 

Format your education history to include the following:

• Degree 

• Name of your university 

• Years studied

• Degree classification of expected grade

• Relevant modules and coursework

Take a look at this data scientist CV example to learn how you might list an education entry: 

BSc (Hons) Statistics: 2:1

Cardiff University

2012-2016

Relevant modules include: Probability and Statistics, Generalised Linear Models, Applied Statistics

You can abbreviate your degree or write it out in full if you have the space; either is appropriate for a data scientist's CV. 

3. Describe your skills.

After listing work experience and education, it’s time to note your skills. List skills in bulleted format for easier readability, and use action verbs where you can. For example, “proficient in JavaScript.”  

Include your technical skills, beginning with those you feel are your strongest data science skills as related to the position you're applying for. You’ll want to list both technical and workplace skills. You don’t necessarily need to set them apart but mention both types of skills. 

If you’re unsure which skills to list or what skills are irrelevant, refer to the job description for which you’re applying (or find a sample online) and match your skills with the ones required for the position or a similar position.

4. List your certifications.

List any certificates you hold that are crucial to the job you're applying for above your experience section. This will highlight these essential skills in a way the hiring manager can easily notice. In your role, you will likely obtain certifications in various programming languages, such as Python, SQL, MySQL, and Git. You might also seek certification specific to the data scientist career field, such as SAS Certified Data Scientist or  Microsoft Certified: Azure Data Scientist Associate.

Continue listing other certifications you hold beneath the most important ones. This includes certifications that are not pertinent to the job yet show you have additional skills that may be helpful to the position.  

Add a header that says "Certifications" and list the following information about your credentials:

• Full title of certification and acronym

• Name of the organisation from which you received the certification

• The date you earned the certification 

More tips for writing a data scientist CV

Keep your CV  concise and informative. Remember that most potential employers spend mere seconds on a CV at first glance, so make yours stand out. Also, remember that experience is key in the case of a data scientist's CV. 

Consider the employers’ viewpoint. 

Point to the skills that employers like to see. Consider common traits employers look for in a data scientist, including:

• Data modelling

• Data analytics

• Data visualisation

• Coding and programming 

• Mathematical skills 

• Machine learning

• Problem-solving 

• Communication skills 

• Teamwork 

Remember to list technical and interpersonal skills so employers can get a well-rounded picture of who you are as an employee and a data scientist. 

If you want to stand out to employees, consider enrolling in a data scientist certification that can be added to your CV. Certifications show employers that you are hard-working and serious about what you want to do in your career. 

Looking to develop job-relevant skills?

Consider courses offered on Coursera, such as Machine Learning and Python, for everybody. These are both in-demand skills for any computer scientist and can give you an edge over the competition on your resume.

Create an elevator pitch.

An elevator pitch is a short, persuasive summary of why someone should hire you. You’re essentially selling yourself. As you gather your information to write your CV, take the time to jot out a short elevator pitch. This simple exercise will help you prioritise what’s important and relevant. 

Take a few sentences from this pitch on your CV to communicate why you are the right person for the job over others. Describe what makes you unique in the field of data science. Highlight the skills and accomplishments most relevant to the position you’re applying for. What makes you the best candidate for this position? 

Be selective about what you include.

Remember, the purpose of a CV is to land an interview. Be selective with what you include. You should generally keep your data scientist CV length to no more than two pages, maybe less if you are a student or recent graduate with little experience.

Be concise in your descriptions and include only relevant information. Think about the things that can catch the attention of the employer. Read about the employer, and do your research. This act alone can help you know what to highlight and what’s probably unimportant to them. 

Remember, projects and work experience are important in data science. Pour your attention into these aspects of your CV. 

Follow a clean, simple format.

The goal is to create a document the hiring manager can easily skim through within seconds. Pay attention to whitespace, use bullet points and bold words for emphasis, and break up large text chunks. 

A good CV should be clean and easy to read. Skip designs with a lot of “extras.” It’s important to include proper headers, consistent formatting (i.e., the same font throughout), and some white space. 

Build data science skills on Coursera.

Data scientists play a crucial role in guiding organisational decision-making with impactful insights. Crafting an effective resume involves emphasising relevant experience, education, and skills in a reverse-chronological format while highlighting achievements and certifications. 

If you're ready to dig deeper into data science, consider the Data Science Professional Certificate from IBM. As an entry-level data scientist, you can develop the skills, tools, and portfolio to have a competitive edge in the job market.