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Machine Learning Resume – Guide & Sample

Last updated:
5th Mar, 2021
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Machine Learning Resume – Guide & Sample

In the current times, getting a Machine Learning job seems quite difficult seeing so much competition around. A Machine Learning Engineer/Data Scientist job posting gets more than 200 applicants within the first day itself. So how to tackle this situation so that you get an awesome Machine Learning job quickly?

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Making a resume is a pain for most job seekers. We will break down each point here to clarity so that the process becomes buttery smooth for you. That’s exactly what this article talks about.

By the end of this tutorial, you will know the following:

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  • What makes a good Machine Learning resume
  • Structure of a sample resume
  • In-depth analysis of each section of the resume
  • Do’s and Don’ts of the resume

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Let’s get straight to it.

What makes a good Machine Learning Resume?

The first step to every job application is the resume. A resume is nothing but a medium of marketing yourself to the recruiter. It’s just equivalent to saying “Hey, here’s what all I have and done. And I’m awesome”. But that’s what an average resume does and miserably fails. A good resume should be a crisp, concise and very structured document that shows why you are right for the job you’re applying for. 

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The ATS Factor

Most of the job postings get 100s of resumes. So do you think the recruiter goes through every resume they receive? No. Most of the recruiters use an ATS (Applicant Tracking System) whose first task is to rate the resumes according to their content.

Once your resume aces the ATS barrier, it goes to the recruiter’s hands who scans your resume for a few seconds. That’s it. Just a few seconds. So our aim here is to make a resume that first passes the ATS barrier and then impresses the recruiter. And then you’re highly likely to receive a call from them. 

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Structure of a Sample Machine Learning Resume

 Below is a sample of a Machine Learning resume that we will discuss. We’d suggest you follow along and make your own resume as you read through. The first and foremost thing to keep in mind while making your resume, and also the mistake which 8/10 people make while making their resume is – your resume doesn’t need to be of more than a page.

Making your resume unnecessarily 2 or even 3 pages long will in no way increase your chances of getting the call. Did you win a race in 7th standard? The recruiter doesn’t care.  The whole idea here is to only include relevant information.

The template shown above is fairly good and tested one. However, you can always make a template of your choice. Also, you can add/remove the sections as it fits your profile and experience. The only thing to be kept in mind is, the simpler it is, the better. Let’s go over each section one by one.


The summary is not really a necessary one if you don’t have any professional experience. The only motive of the summary is to tell the recruiter about your background in 1 or 2 lines. If you are a fresher and don’t have any professional experience, you can skip this one. You can include this even if you have internship experience. 

The major mistake that most of the applicants make is, adding unnecessary adjectives into the summary. For example:

“A highly motivated professional with proven work experience in Machine Learning. A hard-working, goal-oriented and proactive person. I am a team player who is a problem solver and possess leadership skills. Looking for a challenging role to showcase my skills and grow.”

This summary is not at all what the recruiter wants to see the first thing in your resume. And the sad truth is that it is what is present in most of the resumes. Dumping in adjectives like “Highly motivated”, “team player”, etc. will not make the resume stand out. It only makes it more redundant and wastes crucial space. The summary section should talk about how much experience you have, what major skills you possess and what kind of roles you’re looking for.

Contact Details and Social Profiles

This section should contain your phone number, your email address and the current city you’re living in. Do NOT include your whole address up to your PIN code. The recruiter has no interest in it. Keep the location details just to the city, or at most, the state.

Remember, we will only include relevant information. Try to put a professional-looking email address and not something which might put a bad impression on the recruiter. Make a new one if you don’t already have it. You’re going to use that for your entire life.

Put in your LinkedIn profile after customizing the link. Add your GitHub profile only if it has a good amount of projects and activity. Adding a Git link with no or very less activity will put a bad impression. Put in any other relevant links like your blog or website.

Work Experience

This is the most crucial part and the core of your resume if you’re an experienced professional. Include the relevant work experience by making use of action verbs. Keep the points concise and don’t put in too much information. If you’re a fresher and don’t have any work experience, then add the relevant internship experience.

If you don’t even have that, then skip this section and move to the next section and make that the core of your resume. Follow the writing style used in the template above. For checking how good your resume language is go to

Personal Machine Learning Projects

This section should include 1 to 3 good machine learning projects that you’ve made recently. Write about them in short and include the most important details. Do not include beginner-level projects like the Titanic, House price prediction, etc. Adding these will not make your resume stand out. If you’re a fresher or if you don’t have any relevant work experience, then this section should be the core of your resume. Move it to the top and add enough content by making some very good projects.


The skills section should include all the Machine Learning skills that you have- be it algorithms, tools and languages. A great way to make sure your resume clears the ATS is by adding the exact keywords mentioned in the JD of the job you’re applying for. This is because the ATS scores the resumes by the number of matches of keywords in the JD and your resume.

So slightly alter the skills by replacing the words used in the JD. For example, Linear Regression should be changed to Linear Models, if the JD has that. Try to include as many keywords as possible, but do not include the ones you don’t know about.

Hackathon Achievements

This is an additional section and can be skipped. You can also add another section that you’d want to show to the recruiter. Avoid adding your certifications from the MOOCs as they don’t add much weight to the resume. Only add certifications that would be relevant. Such as “Microsoft Certified Azure Specialist”, etc.


The education section should be kept at the bottom if you’re an experienced professional. If you’re fresh out of college, or still in college, you can keep it much above. This section should include the details only of your graduation- the degree, the college/university and the grade/CGPA earned. 

Dos and Don’ts of a Resume

  • Keep it on 1 page
  • Include only relevant information
  • Include keywords from the JD
  • Use action words to describe the experience
  • Remove all the adjectives
  • Avoid including your photo

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Before you go

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We covered all the aspects of a great Machine Learning resume and how to maximize your chances of getting interview calls. The competition for the same job is a lot these days, but you can quickly skip the queue by working on the above point and making sure you don’t commit the same mistake others are committing.

You can use this tutorial as a guide and build your resume from scratch. Just make sure not to just make one resume and use it for all the jobs. Instead, slightly alter it according to the job and the requirements. Just by doing these steps, you’re far ahead of the competition!

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s Executive PG Program in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What skills are required for Machine Learning?

Machine Learning is a subset of Artificial Intelligence and its main application is in Data Mining or Pattern Recognition. It is very useful in developing automated decision-making systems. However, Machine Learning is not limited to that. Machine learning algorithms play an important role in natural language processing and data mining. Although it is an expertise, it should be considered a branch of computer science. Machine learning requires a good grasp on mathematics because it makes use of probability, statistics, and modeling. It is also important to have a strong background in computer programming languages like C, C++, Java, Python, Perl, C# .NET and R.

2How to build a good machine learning resume?

Machine learning is a very hot field these days. If you want to build a machine learning resume, you will need to do some projects in the field. However, you can't just jump into the field without knowing anything about it. We will recommend you to do some pre-work before actually jumping into the machine learning field. You can design a curriculum for getting yourself ramped up for a machine learning role. The curriculum can start with a lot of math, but should go over the fundamentals you need to learn. After that, it should cover different concepts in machine learning. Then it should go over some more math.

3What is the future for machine learning?

In recent years, we have seen a huge increase in the use of machine learning for effective business applications. Machine learning can be used for forecasting of customer behavior, recommending items to customers based on their history, making marketing more effective, etc. A study reported that 80% of businesses that use machine learning have experienced improved customer experience.

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