And being honest, most of them suck.
It’s actually not that hard to write a good resume, yet many people can’t seem to get the basics right.
A good resume is what gets your foot in the door for interviews, so nailing this part of the interview process is essential.
So in this article, I want to walk you through my resume that landed me multiple $100k+ offers across data science and machine learning, and offer my key advice through every section.
The Resume (at a glance)
If you haven’t got time to read through the whole article, this is the resume in PDF format from LaTeX (using Overleaf) and a Google Doc version.
If you want to get this template and learn how to apply it, then check the link below.
The reason I have two versions is that the PDF version generated by LaTeX looks more aesthetically pleasing, but it can be problematic with the Application Tracking System (ATS).
That’s why I have a separate version in Google Doc format, which is much more ATS-friendly.
The ATS is essentially an automated system that helps filter candidates who don’t match well with the job role or description. Like any system, it has flaws and sometimes struggles to parse formats, such as PDF versions produced by LaTeX.
Almost all Fortune 500 companies (99%) utilise ATS for recruitment, and 75% of resumes fail to pass ATS and are never seen by a recruiter.
So, if I am applying in a scenario where there is no guarantee a real human will see it, like LinkedIn quick apply or Indeed job forms, I use the Google Doc format to be safe.
If I know a recruiter, hiring manager or any other physical human will see my resume, then I provide them with the PDF version from LaTeX/Overleaf.
Let’s now break down each section along with my top tips!
The LaTeX template is based from this template by Timmy Chan. You can check the source code here on his GitHub. The template and code are under a Creative Commons CC BY 4.0 licence.
General Principles
I will outline the most fundamental principles that your resume should adhere to. I reckon you might be missing at least one of these points:
- Absolutely no spelling mistakes, and the grammar is correct. This is a big red flag to anyone reviewing the resume.
- Keep it 1 page, unless you have 10+ years of experience.
- Font sizes are consistent across sections and avoid excessive use of bolding and italicisation.
- No fancy formatting, keep it simple.
- No graphics, images or icons.
- Avoid anything that could lead to bias, like age, gender, nationality, etc.
- Use bullet points, not block text.
- For your Google Doc version, use an easy-to-read font, such as Times New Roman, Calibri, or Georgia.
- For your PDF version, I highly recommend using LaTeX, as it produces a clean and aesthetically pleasing resume.
- Finally, if you have time, tailor every section in your resume to the corresponding job description.
Header
This section should be easy to get right, and I am still surprised people get this part wrong at all.
All you need is:
- Your name.
- Job title or how you see yourself. This is optional.
- Contact details, such as phone number and email.
- Location, city and country.
- Relevant links like LinkedIn, GitHub, Medium, Kaggle etc. One thing to ensure is that the links work! They contain helpful information about you, so you want to make sure whoever clicks on them actually goes where you want them to.
Summary Statement
If your resume clearly states what you have done and who you are, then this section is not necessary. You will be repeating yourself and potentially using up valuable space on your resume.
For example, I personally don’t have a summary statement, and it hasn’t affected me (as far as I know anyway!). However, it can be beneficial when you want to tailor it to a specific company or role, or even play the ATS a bit.
One key thing I will say, It’s really easy to go wrong here using words like “passionate”, “hard working” or “determined.”
Don’t do that.
I can’t tell you the number of resumes I have seen where the person claims to be a hardworking, motivated, and passionate individual.
That’s the baseline of anyone a company wants to hire.
A good summary statement explicitly states who you are and what you have done in a couple of sentences.
For example, if I were to do this for myself, I would write.
Data Scientist / Machine Learning Engineer with 4+ years experience specialising in time series forecasting, operations research / optimisation problems and applied machine learning. Domain expertise lies in Insurance, supply chain and logistics business areas from the various companies I have worked in.
No fluff, just straight to the point.
Technical Skills
This is a very brief summary of all your abilities, and should not really exceed 4–5 lines and that is pushing it.
This is high on the resume because it will immediately help the recruiter know if you meet the job’s technical requirements.
Now, typically, candidates introduce many red flags in this section without even realising it. They think they are adding things that recruiters and hiring managers want to see, but it’s actually the exact opposite.
Let’s break down the most common mistakes:
- Don’t list too many technologies; this looks suspicious. I would be sceptical if you listed something like “Python, SQL, C++, Rust, Assembly.” It seems like a bunch of buzzwords, and I would find it highly unlikely you knew them all to a reasonable level.
- When it comes to coding abilities, it’s best to use language like “proficient” or “familiar with.” Avoid using arbitrary star ratings like “Python 4/5” or claiming to be “advanced.” This way, you’re setting realistic expectations and ensuring that your skills are accurately represented. My criterion is that if you feel comfortable answering easy Leetcode problems in that language, then you are proficient.
- Don’t write out every Python package you know. If you are applying for a data science position, I assume you are familiar with NumPy, Pandas, and Matplotlib; there is no need to explicitly state this. Instead, list things like Git, AWS, Argo, Bash and Databricks, which are actual technologies that not every candidate will have.
Experience/Projects
The most important part here is to demonstrate exactly what you have done at each company and what the outcome was, always using numbers and figures. Ideally, they would be financial impact.
Don’t try to be too humble; really “flex” the work you have done and the impact you have produced. Really showoff your skills.
For example, notice in my resume how I discuss technical steps or models like “ARIMAX” or “XGBoost” with the goal of better forecasting or predicting some business problem, mentioning the improvement of the model using some metric, and finally tying that to business impact.
This shows my technical abilities and that I think of business impact with my projects.
If you think about it, companies only care about the financial benefit you bring them. Whether you use a neural network or linear regression, it doesn’t matter.
Profit is profit.
It may seem reductive, but it is true, so if you can showcase precisely how you know how to link technical topics like machine learning to business outcomes, then you are doing better than like 80% of applicants.
This is the framework I recommend you follow for every bullet point in the experience section:
- State what you were analysing, predicting or modelling.
- State the technologies, algorithms and statistical tools you used.
- State the metrics you improved.
- State the business value you generated.
Another thing is don’t be scared to be explicit about the exact technologies, packages and algorithms you used. It’s better that way than using vague language, and it will also allow you to hit the ATS better.
Some extra, but arguably obvious things are:
- Only include paid work experience, but research experience is ok too.
- Start with your most recent job and work backwards chronologically.
- Differentiate between internships and full-time positions.
- Don’t use sub-bullet points; they are not necessary.
If you haven’t got any experience, replace this with a projects section and carry out similar wording regarding the technical and business parts. Try to list projects most relevant to the role you are applying for to demonstrate an interest in that particular area.
Education
If you don’t have any relevant work experience, I recommend putting the education section before work experience and then a projects section after.
As I have 4+ years of experience, my education section is pretty simple. I keep it in as many data science and machine learning jobs explicitly state a need for a master’s degree in a STEM subject.
If you don’t have experience, you could flesh this education section out and discuss any relevant work you have done in your degree. However, I don’t recommend listing out all your modules as that’s a bit overkill, and being honest, no one really reads that or cares. A few extra things to take into account
- If your grade is impressive, list it; if not, leave it blank.
- List any relevant specialised thing you have done, like hackathons, projects etc.
- List any awards and prizes you achieved whilst at school.
- Leave out course certificates, unless it’s something like the “AWS practitioner ones” or you have loads of space left.
Activities / Extracurricular
This is optional, and many people will say don’t add this section.
I controversially think, however, that showing a little bit of personality in your resume is not a bad thing, but it’s definitely not needed, and this would be the first section to cut if you are lacking space.
I use this part to showcase my YouTube channel and blog posts, as it adds to my resume and application, but this is a rare case.
So, if you have something similar you want to mention, this section is excellent for that purpose.
Earlier, I discussed how if you don’t have any experience, you should replace this with a projects section.
But what projects should you do to get hired?
Well, this is precisely the question I have answered in one of my previous articles, which you can check out below:
Another Thing!
I offer 1:1 coaching calls where we can chat about whatever you need — whether it’s projects, career advice, or just figuring out your next step. I’m here to help you move forward!
1:1 Mentoring Call with Egor Howell
Career guidance, job advice, project help, resume reviewtopmate.io
