Are you looking for tips on how to write an entry level data science resume that will get you more calls from recruiters and hiring managers? Well then you are in the right place! In this article we discuss all of our best tips for writing an entry level data science resume that will stand out to recruiters.
We start out with a discussion of how to format your resume to ensure that it is easily skimmable and highlights your most important skills and attributes. After that, we discuss what content you should include in an entry level data science resume. This includes high level details about what sections should be included in the resume as well as specific information about what details should be included in each section. Finally, we will discuss a process for creating an entry level data science resume that will help you create a resume that is clear and focused.
How to format an entry level data science resume
Before we talk about the actual content you should include in an entry level data science resume, we will discuss how you should format an entry level data science resume. Why are we talking about this first? Because hiring managers and recruiters often make decisions about whether to extend an interview invitation to a candidate after looking at their resume for just a few seconds.
This means that the way you organize and format the content on your resume has a large impact on whether you get an interview. Two resumes with the same exact content might achieve different outcomes if one resume is easily skimmable with the important content highlighted at the top and the other is dense and disorganized.
Before you think about what content you are going to include in your resume, you should think about how you are going to format your resume so that it conveys the most important concepts in the least amount of time. Throughout the rest of this section we will provide tips on how to format an entry level data science resume. We will start out by discussing high level concepts like the length of an entry level data science resume then work our way towards more specific details about how to format your resume for better readability.
Length of an entry level data science resume
How long should an entry level data science resume be? This is the first question we will answer in our discussion of how to format an entry level data science resume. When you are writing an entry level data science resume, you should make sure that the contents of your resume fit comfortably on one page. There are very few, if any exceptions to this rule.
If you find that you are having trouble fitting the contents of your resume on one page, then you should take a step back and refocus. Think about the main points that you are trying to convey in your resume and then remove content that does not support these points. It is better to have a focused resume that clearly demonstrates a few key strengths than a scattered resume that reads as a list of tasks you have completed.
We will note that if you are asked to provide a CV, or curriculum vitae, in place of a resume then it is okay for your document to be more than one page long. CVs generally contain additional sections that are not included in resumes such as lists of papers you have published and presentations you have given. These lists can get quite lengthy, so it is okay for them to take up more than one page.
Formatting content for readability
Now that we have discussed the length that an entry level data science resume should be, we will go into more detail about how you should organize content within your resume. Here are some tips for formatting content within sections so that it is easy to skim.
- Use bullet points when possible. Whenever possible, you should use short bullet points rather than long form paragraphs in your resume. This makes it easier to skim your resume and jump around from bullet point to bullet point.
- Use a maximum of 2 short sentences at a time. You should do your best to make sure that each bullet point within your resume is no longer than 2 sentences long. If you are having trouble condensing a point into 2 sentences, consider whether you should break that bullet point out into multiple points.
- Always list jobs in reverse chronological order. You should always list different jobs and degrees you add to your resume in reverse chronological order. It may be tempting to list your most relevant jobs first, but you should resist the urge to do this. It will make it more difficult to understand your background at a glance, which will result in recruiters spending more time trying to piece together a timeline of your background and less time actually digging into your experiences.
- List projects within jobs in order of importance. Within a specific job, feel free to list the projects that are most relevant first. There is no need to list your individual projects in reverse chronological order.
Formatting content for clarity and impact
Now that we have talked about some tips for formatting the content in your resume so that it is easier to skim, we will talk about formatting the content to quickly convey the main takeaways.
- Use common english. Whenever possible, you should use common english instead of highly technical terms. Explain your projects as if you were explaining them to someone with a limited technical background. Recruiters are more likely to give you an initial phone screen if they can understand the high level details of your projects. That is not to say that you cannot use technical terms to describe the methods you use, but make sure the main goal and impact of your project are stated in plain english.
- Mirror the language in the job description. When it comes to describing the technical methods you used to complete your projects, you should try your best to mirror the language that is used in the job description. A recruiter who is looking for someone with experience with random forests might not know that random forests and tree based methods have similar meanings.
- Do not leave recruiters to infer any important points. You should directly state important points that you want recruiters to take away from reading your resume rather than leaving them to infer anything on their own. If you are adding a bullet point to a job description to demonstrate your leadership skills, you should directly state how you exercised leadership skills. Do not just state what you did and assume that the recruiter will infer how that demonstrated leadership skills.
- Focus on outcomes when possible. If you have worked on any projects that had material outcomes, you should make sure to focus on the outcomes of your projects over technical details of your methodology. Of course, you should mention what kind of methods you used, but the main focus should be on what you achieved rather than how you achieved it. Recruiters like to see that you understand the value that your projects bring because ultimately you are being hired to deliver value to the company.
- Repeat important points. Do not be afraid to repeat important points throughout your resume. This will only reinforce the important takeaways you want recruiters to walk away with. For example, if you want to emphasize your skills with data visualization then you should mention your data visualization skills in your summary, skills, jobs, and personal projects.
Content for an entry level data science resume
Now that we have given some pointers on how to format an entry level data science resume, we will provide more details about the content you should include in an entry level data science resume.
Summary or highlights
You may want to include a section at the top of your resume that summarizes the most important takeaways that you want recruiters to come away with after reading your resume. This section should be at the top of the resume because it will help direct recruiters attention and focus it on the key takeaways for the start. Here are some examples of things you can include in your summary.
- List years of experience with certain skills. You can use this section to list the number of years of experience you have with key skills such as your preferred programming language or statistical analysis software. This will make it easier to skim your resume and determine your level of experience you have.
- Clarify your primary area of expertise. If you mention many different technologies and methodologies throughout your resume, then it might be difficult to determine exactly where your area of expertise lies. The summary section is a good place to clarify where your primary area of expertise lies. Do you have extensive experience with natural language processing? Consider calling that out in your summary section.
- Directly mention your key strengths. You should also mention the key strengths you want to emphasize in your summary section. For example, if one of your strengths is presenting technical concepts to non-technical audiences then you should include a bullet point directly mentioning that strength.
The skills section of your resume is the section where you should list the methods and technologies you are familiar with. You should always include a skills section in an entry level data science resume. Here are some tips for deciding what to include in your skills section.
- Only list skills you can competently discuss. You should only list skills that you are comfortable answering basic to intermediate level questions about in your skills section. It is better to have a smaller skills section that is restricted to skills you can competently speak about than a larger skills section that contains skills you have only peripheral knowledge about. If you are unable to speak competently about one skill on your resume, it will cast doubt on your familiarity with all of the other skills listed.
- Do list skills you gained working on personal projects. You should feel free to list skills that you gained through working on personal projects even if you have not used these skills in a job or internship. There are many skills that you can gain a deep understanding of through working on personal projects.
- Break your skills section into smaller subsections. You should break your skills section into at least two clearly marked sections. The first should include technologies & programming languages you are familiar with such as Python, R, SAS, Docker, and Git. The second should include statistical concepts & machine learning models that you are familiar with such as experimental design, hypothesis testing, linear regression, causal inference, and neural networks. You may also want to include a third section with soft skills or other skills that do not fit neatly into the first two sections. This will depend on what skills you are emphasizing in your resume.
The work experience section can be difficult to fill out on an entry level data science resume because many entry level data scientists do not have much tangible work experience. Here are some tips on how to think of content to put in your work experience section.
- Think of school as your job. When you are a student, school is considered your full time job so you should feel free to list real world experience you got as part of a class or school related activity as work experience. Did you take on a role as a research assistant or a TA to help fund your education? Did you take a consulting class where you worked on real world problems? Feel free to list this experience as work experience.
- It is okay to include jobs in other fields. It is okay to include jobs you have had that are not directly related to data science if you do not have any relevant work experience. If you do this, you should think carefully about how those jobs provided you with skills that are relevant for data science jobs and highlight those skills. Data related roles often require just as many soft skills as technical skills and many of these soft skills can be developed in unrelated positions.
- Consider freelancing. If you are having trouble bulking out your work experience section and you feel like this is really holding your back, you should consider doing some freelance work. Even simple projects that involve organizing data or providing descriptive insights about a dataset are better than nothing.
Projects to improve resume
If you have worked on personal projects to build your data science skill set or enhance your resume, you should feel free to list these on your resume. If you are wondering what kinds of projects look best on your resume, then you should check out our standalone article on data science projects for your resume. Here are some thoughts you should keep in mind when deciding what content to put in this section.
- Clearly differentiate personal projects from work experience. Make sure that you clearly differentiate personal projects that you have worked on in your own time from your work experience. Some applicants list their personal projects and work experience in the same section, especially if they are entry level applicants that do not have much work experience. The problem is that this can make it difficult to understand exactly what was a personal project and what was not. You should aim to make your resume as clear as possible so that recruiters spend time focusing on what your skills are rather than trying to piece together details about your background.
- Focus on the most relevant projects. Are you someone who has worked on a wide array of personal projects? You are better off selecting a small subset of the projects that you have worked on and discussing them in detail. Remember that your resume should be a highlight reel of your most relevant experience, not a list of everything you have ever done.
- Feel free to include school projects. If you have not worked on personal projects in your own time, but you have worked on projects you are proud of for school then you should feel free to list school projects on your resume.
- Provide links to your code. You should provide links to the code you used to create your personal projects whenever possible. Not all recruiters and team members will look at your code, but some will. Having one or two clean code samples is a great way to build some credibility by showing your interviewers direct examples of your work. Make sure that any code examples you show are clean and well documented.
You should also include a section that contains information about your educational background and the degrees you have received. At a minimum, this section should include information such as the school you attended, the years you were there, and the degree you received. Here are some other data points you might want to include in your education section.
- Relevant classes. If you are just recently out of school then it may make sense to list the information about classes you have taken in your education section. For example, if you are applying for jobs that want candidates with experiences in causal inference and you took multiple classes in causal inference at school then you should list that in your education section.
How to create an entry level data science resume
Are you having trouble getting started on your entry level data science resume? In this section, we will discuss a simple process you can follow to create an entry level data science resume that is clear and focused.
- Create a master resume document. The first thing you should do when you are starting a new resume is put together a master document (often referred to as a master resume) that contains all of the possible information you might want to include on your resume. You should list every relevant project or job you have worked on and include as much relevant information as you have about each. Having all of your experiences listed out on paper will make it easier to sift through your experiences and determine which ones are the most important ones that you want to highlight.
- Choose a few key strengths to emphasize. After you have listed out all of your experiences, you should look at themes that reoccur often and decide on 3 – 4 key strengths that you want to emphasize on your resume. Here are just a few examples of key strengths that you might want to emphasize on an entry level data science resume.
- Deep expertise with one specific programming language or tool
- Broad expertise across a variety of programming languages or tools
- Deep expertise in on area of machine learning or statistics
- Broad expertise across many areas of machine learning and statistics
- Knowledge of model deployment tools
- Data visualization
- Technical writing
- Technical presentation
- Presentation of technical concepts to non-technical audiences
- Communication and collaboration
- Mentoring and leadership
- Software engineering and clean coding practices
- Creativity and problem solving skills
- Project management and organizational skills
- Independent learning
- Create an initial draft. After you decide on the key strength that you want to emphasize in your resume, you should create an initial draft of your resume. You should focus on the key strengths you decided to provide some coherency to your resume. Provide examples that demonstrate those strengths whenever possible.
- Refine and reduce. After you create your initial draft, your next goal should be to go through the resume and remove anything that is not absolutely crucial. You are generally better off having a smaller amount of content that is clearly written and coherent than a long list of everything that you have ever worked on. For each point on your resume, evaluate whether it demonstrates one of your key strengths or highlights a core competency that is required for data science jobs. If it does not do either of those things, remove it.
- Get feedback and refine further. After you have created a refined version of your resume, you should get feedback from as many people as possible. You do not have to follow every piece of feedback that was provided by every person, but you should certainly look out for trends and common lines of thinking that you hear from multiple people. This is the absolute most important step in the process because it ensures that your resume is easy to read for other people who do not have the same background or domain knowledge you have.
How to customize an entry level data science resume
Do you have questions about how much to customize your entry level data science resume for each job you apply for? Here are our suggestions for customizing an entry level data science resume.
- Create a template resume for each type of job. The first thing we recommend doing is creating a template for each type of job you want to apply to. For example if you plan on applying to machine learning engineer, data scientist, and data analyst roles, then you should create one base resume for each type of job. You should highlight different strengths across the different resumes. For example, in a resume for machine learning engineer positions you should focus on software engineering skills. For a data analyst role, you should focus more on data wrangling and data visualization.
- Make minor tweaks to language and skills for each job. Once you have a template resume for each type of position, you can make minor tweaks to mirror the language and specific skills that are mentioned in each job listing. The degree to which you tweak your resume may differ for different companies. For companies that you are very excited about, you may want to customize your resume a lot. For other companies, you may not need to tweak your resume as much or at all. No matter what field you are in, applying to entry level jobs is at some level a numbers game. Many of the job openings that you see listed may already be filled, have internal candidates lined up for them, or be about to be rescinded due to budget cuts. If you have time to customize all of your applications then go for it, but if your time is limited then you are better off submitting 10 resumes that are lightly customized than 2 resumes that are highly customized.
Any other questions?
Feel free to leave us a comment if you have any general questions about creating an entry level data science resume. Remember, if you have a question then it is likely that someone else has that same question too!
If you are looking for a mentor to assist you with creating and revising your resume, feel free to reach out to us at email@example.com! We have multiple team members that have been involved in hiring data science, analysts, and engineers across multiple organizations. Note that we charge an hourly personal career consulting rate for these services.