Are you wondering how to explain machine learning projects in your resume? Well then you are in the right place! In this article, we tell you everything you need to know to understand how to explain machine learning projects in your resume.
We start out by discussing who your audience is when you write a data science resume. After that, we discuss what your audience wants to learn when they read descriptions of your previous projects. Next, we go into more detail on what you should and should not include in descriptions of machine learning projects. Finally, we lay out some guiding principles you can follow in order to write compelling project descriptions that will get you calls back on your resume.
Who is your audience when you write a resume?
Who is the audience that will be reading your resume? This is the first question that you should think about when deciding how to explain machine learning projects in your resume. Why is it important to keep your audience in mind? Because this will ensure that you are writing project descriptions that can be read and understood by everyone in your audience, rather than descriptions that are only accessible to a subset of your audience.
So who actually reads data science resumes? Here are some common examples of people who might read a data science resume and make decisions about whether you are a good fit for a particular role.
- Recruiters. In many cases, the first person who will read your data science resume is not a data professional at all, but rather a nontechnical recruiter. The recruiters who sift through data science resumes often have minimal knowledge of machine learning and do not understand the intricacies of complex machine learning models. That means that if your resume can only be understood by those who have deep knowledge of machine learning, it is unlikely that your resume will make it through the initial screen. Getting an initial call with a recruiter is often the hardest part of the hiring process, so it is absolutely critical that you write your resume in a way that is accessible to nontechnical team members.
- Data professionals. After your resume makes it through the initial recruiter screen, it will then be viewed by data professionals and people with more knowledge of machine learning. That means that while the main points of your resume should be accessible to people who come from nontechnical backgrounds, your resume should also contain details that will be interesting to technical audiences.
What does your audience care about?
In addition to thinking about who your audience is, you should also think about what motivates your audience. You should keep in mind the questions that your audience members ask themselves when they read your resume. This way, you can provide your audience with answers to those questions that will reflect positively on yourself and your experiences.
Here are some common examples of questions that your audience members might ask themselves when reading descriptions of machine learning projects.
- Are you familiar with the technologies they use? The first question that your audience might ask themselves is whether you are familiar with the technologies that they use. When we say technologies, this might mean anything from a programming language or framework to a machine learning model or statistical test.
- Are you familiar with the domain they work in? The next question that your audience might ask themselves is whether you are familiar with the domain that they work in. Domain knowledge is an important prerequisite for success in machine learning projects, so your audience will want to understand how much time you will need to ramp up and learn about the domain they work in.
- What kind of work can you complete independently? Another question that your audience will ask themselves is what kind of work can you complete independently. Will you need oversight to complete anything other than well-defined tasks, or do you have a track record of leading large cross functional projects?
- Do you have a history of delivering business value? Another question that your audience will ask themselves is whether you have a track record of delivering business value. Ultimately, they are looking for someone who can contribute to the team’s success by delivering business value, so they will want to understand what type of value you have delivered in the past.
How to explain machine learning projects in a resume
So how do you explain a machine learning project in a resume? In this section, we will discuss how to explain machine learning projects in your resume. We will start out by detailing the most important pieces of information you should include in your project descriptions. After that, we will provide pointers that you can follow to ensure that your project descriptions are clear and compelling.
What information to include in descriptions of machine learning projects
What information should you include when you describe machine learning projects in your resume? In this section, we will describe the most important pieces of information that you should include when you explain a machine learning project in your resume.
- Business problem solved. The first piece of information you should include when describing a machine learning project in your resume is information about the business problem that your project solved. After reading the description of your machine learning project, your audience should understand why the project was important and what pain point the project solved.
- Scale of business impact. The next piece of information you should include when describing machine learning projects in your resume is the scale of business impact that the project had. You should try your best to quantify this impact and provide any additional figures that would be required to understand the scale of that impact.
- Your role in the project. The next piece of information that you should include when describing machine learning projects in your resume is the role that you played in the project. If you were part of a large team that contributed to the project, then make that known. If you were the point person who was leading the team, then mention that.
- Technologies used. Finally, it is a good idea to briefly mention the technologies that you used to complete the project. You do not need to provide a comprehensive list, but you should mention the most important technologies that you used.
What to keep in mind when describing machine learning projects
In this section, we will lay out some guiding principles that you can follow to ensure that the descriptions you provide for your machine learning projects are clear and compelling.
- Speak the same language as your audience. The most important thing that you should keep in mind when writing descriptions of machine learning projects is to use the same language that your audience speaks. Be conscious of who your audience is and make sure to use terms that are accessible to them.
- Keep descriptions as concise as possible. You should also aim to keep your machine learning project descriptions as concise as possible. Most people only spend a few seconds looking over a resume before deciding whether to keep the resume or pass on it, so you should make sure that your project descriptions can be skimmed in a short amount of time.
- Favor outcomes over outputs. As you write descriptions of your machine learning projects, and particularly as you describe the impact that your machine learning projects had on the business, you should favor outcomes over outputs. This means that you should favor descriptions of how your project improved measurable business metrics over descriptions of the output of your project. Even if the output of your project was perfectly designed, it did not contribute to the success of the team if it did not impact business metrics.
- Do not leave anything open for interpretation. Finally, you should make sure to clearly lay out any connections that you want your audience members to make. Do not leave anything open for interpretation and assume that your audience members will make the right connections. You audience members do not have the level of context that you do and they very well might make the wrong connections.
