I have reviewed thousands of tech resumes from candidates who are interested in working as data scientists, machine learning engineers, data engineers, or software engineers. There is a common pattern of mistakes that almost all candidates make.
Here is a list of the most common resume mistakes and simple solutions for avoiding them.
Not using an available template
Unless you’re designing a creative and out-of-the-box resume, it’s best to use a template to avoid formatting issues. There are lots of freely available templates in LaTex, Microsoft Word, and Google Docs.
Technical skills not reflected in past experience
You should always list all your technical skills (languages, libraries, techniques) on your resume. What’s more important is to reflect all those skills in your past experience or projects to demonstrate how you’ve utilized them in the past.
Vague descriptions of past experience or projects
Make sure all descriptions are clear and detailed. Try to quantify as much as possible to provide a clear picture to the reader. Each bullet point under an experience or project should clearly describe what the situation was, what you did to resolve it and the tools and techniques you used, and the outcome of your efforts.
No links to Github repositories for personal projects listed
While most candidates include a link to their Github profile, they fail to include a link to the specific Github repository associated with a project. This is so simple yet very important. Make it very easy for hiring managers/teams to find your work. Don’t assume that they will search your Github profile to find the projects you’ve listed on your resume.
Finally, look at other people’s resumes in your field for inspiration. Review those resumes assuming you’re hiring for the role and see if you spot any mistakes and make sure you’re not making those mistakes in your resume.