Data Science CV Example

A data science CV that includes all the necessary details and is tailored carefully to the job description puts you in a great position. It can help you pass the ATS screening stage, make a strong impression with the employer and reach the latter stages of the recruitment process. Let’s break down the core components of a CV and examine how to build them effectively.

Key sections of a data science CV

Your strategy for writing a data science CV will depend heavily on your experience, your level of seniority and the requirements listed in the job description.

However, at any stage of your career, a data science CV serves as a professional biography that must clearly illustrate your career trajectory. To help you tell that story effectively, we will now break down the document piece-by-piece, starting with your contact header and moving through the key pieces of your professional path.

CV Header

Kick off your data science CV with a header listing the essential contact information such as your name, email address, phone number and location. You don't typically need to include your full address. Incorporate design elements that set the tone and design language of your document. Additionally, including your LinkedIn profile as a URL can be useful, as it will help the reader to quickly and easily access further information about your career and credentials.

When you're applying for jobs in the UK, it's generally not advisable to include a photo or more personal details than are strictly necessary, such as your age, gender, ethnicity or nationality. Including these can jeopardise the recruitment process by introducing bias, and can fall foul of the Equality Act 2010.

CV Summary or Objective

Under your header, write a brief CV summary or CV objective, outlining a few of your key skills, qualities and achievements. This short paragraph can help employers to quickly assess your suitability for the role, setting the tone for your data science CV. While a CV summary showcases your key skills and achievements in the context of your career to date, a CV objective provides an alternative approach. It focuses instead on your ambitions for the future, making it ideal for junior candidates without much work experience.

Whether you choose to write a summary or an objective, aim for a length of two or three sentences, introducing your key skills, unique qualities and key achievements or ambitions, making sure they reflect what's included in the job description.

An effective summary will include brief reference to one or two of your strongest skills, ensuring they reflect the skills listed in the job description. It's important to make your skills and qualities feel unique to you, and show how you've used them to positive effect in your career to date. Below you'll find an example of a strong data science CV summary.

See above for an example of an ineffective summary, with subtle differences leading to a reduction of impact. An ineffective summary might be vague or generic, failing to highlight specific personal qualities that help you stand out and failing to address the requirements specified in the job description. They might also lack firm evidence of your skills, and be structured with long, hard-to-read sentences.

Work Experience

The work experience section of a CV is usually the most important part. Employers look for evidence of how you've developed and used your skills to good effect in your career to date, as an indication of your likely future performance. Always tailor this section of your CV, focusing on keywords and phrases that match the job description, so employers can assess how you might put the same skills and qualities to good use in the future.

List only relevant previous jobs, and add your job title, the name of the employer, its location and your dates of employment. Under this, write several bullet points showing employers how your skills and key qualities contributed to positive outcomes.

To differentiate your work experience section from other candidates, include action verbs and quantifiable evidence that showcases the impact you made. Show your career progression through the skills you developed and used in each role. See below for an example of a strong work experience section for a data science CV.

Above you can see an example of what not to do with your data science CV work experience section. An unengaging work experience section could be too generic, focusing too much on day-to-day duties rather than skills and achievements. It could also fail to address the job description or lack evidence to show the impact you've made in your career to date.

Education and Qualifications

With your education section, you'll draw attention to your most recent and highest qualifications, particularly emphasising any qualifications listed as a requirement in the job description.

For working in data scientist positions, it's essential to have a relevant university degree, and as such, you'll want to feature it in your CV. Include your Bachelor of Science in Data Science or another related degree that qualifies you for the role, in your CV, along with any other degrees or qualifications that highlight your strongest key skills, including statistical modelling techniques or machine learning algorithm development.

Creating the education section of your CV means selecting the most relevant and highest qualifications, and listing them in reverse-chronological order, starting with your most recent achievements and working back from there. For each entry, include the name and level of the degree or certification, the institution, its location and your graduation date or dates of study. To emphasise your qualifications and achievements, you might wish to include one or two bullet points, which highlight things like specialist areas of study, projects, dissertations or society memberships.

If the job description requires any specialist certifications or licences, you may wish to add these in your education section. If you add these, it's also a good idea to include the expiration date of the licence or qualification, if it has one.

Skills

In your CV's skills section, you'll want to draw attention to some of your strongest skills that make you suitable for the role. Review the job description to get an idea of the most essential skills, and create a list of hard and soft skills, including some of your strongest, most unique qualities that set you apart from other candidates. In a data science CV, focus on the most relevant and essential skills in your skills portfolio, such as communication and SQL database query optimisation, to show you're qualified for the data scientist position and to put you in a strong position to progress.

Hard Skills

Hard and technical skills are the essential skills required for carrying out the everyday duties of the role. They might include specialist operation of certain software or equipment, or knowledge of certain industry standards and regulations. You could gain these skills via training, certifications or industry experience. For data scientist jobs, critical hard skills you've gained in your career can include data visualisation design principles, and statistical modelling techniques. Firstly, check the job description, then add four or five key hard skills in your CV that help the employer to decide if you're a good fit for the role.

The ideal hard skills section will feature the most essential hard skills from the job description, while closely reflecting your own best technical abilities. The closer your strongest skills are to matching the job description, the higher your chances of success.

Take a look below to see the type of skills that are commonly listed in a data science CV hard skills section:

Soft Skills

Soft skills are the personal strengths and qualities that show employers how well you'll fit into the role and complement other members of the team. Soft skills tend to be more transferable and applicable to different roles than hard and technical skills. The world of work is evolving at a rapid pace, changing the types of hard skills required for many roles, and therefore rendering soft and transferable skills more valuable than ever. Soft skills are also extremely valuable for junior and entry-level roles, where candidates aren't necessarily expected to have a wealth of relevant work experience.

Adopt the same approach as you did with your hard skills list, reviewing the job description to understand the requirements, before assessing which soft skills you can provide evidence for throughout your data science CV. Draft a list of up to five key soft and transferable skills, combining the most essential skills from the job description with your strongest personal qualities.

Below is a selection of soft skills regularly featured in a data science CV.

Language Skills

Adding foreign language skills to your data science CV can be a valuable addition that reflects well on you as a candidate. Even if language skills aren't listed as a requirement in the job description, if you speak a foreign language, it can be beneficial to add it to your CV. List any foreign languages you speak, together with an indication of your proficiency level.

There are a few acceptable ways of citing your foreign language proficiency levels. The simplest way is to assign a basic descriptive word to indicate your skills, such as:

• English: Fluent
• Spanish: Intermediate

You could otherwise use an internationally recognised language standard, such as the Common European Framework of Reference (CEFR). This assigns your language skills a standardised level of competence, as follows:

• A1: Beginner
• A2: Elementary
• B1: Intermediate
• B2: Upper intermediate
• C1: Advanced
• C2: Proficiency

Certifications and Licences

If you have extra qualifications beyond the basics of what's expected or required for the role, you might want to include a separate certifications section in your CV. It can be a valuable way of differentiating yourself from other candidates and showing employers your dedication, motivation and commitment to professional development. In addition, the certifications section can be a valuable addition to your data science CV if you're applying for a role that cites certain certifications or licences as a necessity in the job description. These might include roles where the use of specialist software or equipment forms part of your everyday duties.

See below for a list of example certifications and licences you might add to your CV for data scientist roles:

Additional Information

Optional sections can be useful to add to your CV, to provide additional evidence that you have the skills for the data scientist job. Consider adding optional sections if you're unable to show all the necessary skills for the job through work experience, but could show them through extracurricular activities and other areas of life. This could be especially relevant if you're a junior candidate, or if you're changing careers.

If you're curious about other ways to make your CV more effective, our career resources will help you strengthen your application.

Hobbies and Interests

Your hobbies and interests can be a useful way of showcasing additional skills that are relevant to the job description, but that you haven't been able to prove via your work experience. In addition, hobbies and interests can showcase your personality, helping to differentiate you from other candidates. However, it's important to only mention hobbies and interests that are relevant, or related to, the role you're applying for. If your hobbies don't help you to show skills required for the role, that are missing elsewhere in your CV, it's best to leave this section out.

Key Achievements

Creating a section for your achievements and awards can help you draw attention to the things you're most proud of in your career to date. Add any awards you've won or career milestones you've reached, so employers can easily see the impact you've made in your career to date.

Volunteering

Another way of showing employers your skills and experience is through volunteer roles. If you're struggling to show you have the necessary credentials through your work experience, volunteering can provide valuable examples of how you've put your skills into action. Approach your volunteering section in much the same way as your work experience section.

For each entry, include a job title or description of your role, the organisation, its location and the dates you volunteered. Adding bullet points can also help you to show how you developed relevant skills, and used them to good effect.

Top action words to use in a data science CV

Starting each of your work experience bullet points with strong action verbs is a great way to showcase your key skills and qualities, and demonstrate the impact they've had in your career to date. Start each bullet point with a verb linked to the skills required in the job description, to add focus to your work experience section and make it easy for the reader to identify your strengths. When adding action verbs to your work experience bullet points, just remember to always provide quantifiable evidence that shows the value you added for each employer. Use past tense for any action verbs that describe previous roles (for example, 'developed') and present tense for current roles (for example 'collaborating').

Data science CV example

Now that you know exactly what to include in your data science CV, we can take a look at a final, finished example below:

To get an idea of how your completed, one-page CV will look once its been fully designed, see our selection of CV examples.

How to make your CV ATS compatible

Many employers now use applicant tracking systems (ATS) to assist with managing the recruitment process. One of the key elements of most ATS apps is the ability to scan CVs and rank them according to their likely match to the job description. By assuming this role in the recruitment process, ATS apps can reduce the amount of time employers need to spend reviewing CVs. With hundreds of applications for a single vacancy becoming increasingly commonplace, this increased efficiency is extremely valuable for employers.

The increasing usage of ATS apps by recruiters and employers means it's critical to adapt and prepare your applications to successfully navigate this stage of the selection process. Following the tips below will give you everything you need for an ATS-compatible CV:

• Include keywords and phrases that match the job description, giving you the best chance of appearing as a strong fit for the role.
• Use standard CV headings that clearly identify each section, such as 'work experience', 'education' and 'skills'.
• Opt for a simple CV layout with consistent formatting, avoiding any special design elements that could make your CV harder for ATS apps to scan.
• Select a widely-used font in either serif or sans serif style, with a font size between 10 and 12 for body text and 14 and 16 for heading text.
• Use bullet points in place of full sentences and paragraphs. This can reduce the overall length of the document, make the keywords stand out and make it easier for ATS apps to scan.

You might feel there are a lot of things to remember when writing an ATS-compatible CV, but with just a few small tweaks, you can ensure yours passes this stage. Use one of our expert-designed, ATS-compatible CV templates to avoid the stress of adapting your CV for ATS screening.

Key takeaways for an impactful data science CV

To stand out from the crowd with your CV, tailor your approach to each individual application, incorporating keywords and phrases that match the job description. Select a suitable CV format that reflects your experience level, and focus on highlighting your key skills, and demonstrating how you've put them to good use to achieve positive outcomes in your career to date.

Finally, creating your CV using one of Jobseeker's expert-designed CV templates can give your application the edge, placing you among the leading candidates and positioning you for success with your job applications.

Sources:

1. Barnet Council (UK local government), Recruitment tips: How to write a supporting statement
2. Jobseeker, HR Insights