6 years. That is how long I have been hiring for data science and machine learning roles. I built the models that run a resume screening and candidate selection application. I have researched hiring and how people make hiring decisions.
What have I learned? Most resumes are badly written. The most common problems are:
Hiring has a lifecycle just like model development. Your resume needs to be built to stand out at each phase. What are they?
Automated Resume Screening: Once you submit your resume, it is parsed by an application. Your resume must be formatted to score well and advance to the next phase.
HR and Recruiter Screening: The resume selection application creates a shortlist of candidates for someone in HR or a Recruiter to manually review. They are looking at the role through a semi-technical, sometimes partial understanding of the qualifications. Your resume needs to target their decision-making process.
Hiring Manager: The hiring manager is looking for indicators that you can be successful in the job. They are bombarded by keyword heavy resumes. Most candidates do not get an interview because their resumes focus on skills not capabilities.
A well-built resume results in recruiters reaching out to you rather than you applying for hundreds of jobs to only get a few responses. Automated resume screeners are looking through resumes, LinkedIn profiles, Github repos, and other external sources daily. Even without submitting your resume, you should be getting regular emails and phone calls for jobs.
If that is not happening, it is time to rebuild your resume.
Resume screeners are a lot like search engines and there is a resume version of optimization for discovery. Formatting is especially important. Parsers range from a massive RegEx to NLU based systems. Your resume needs to be written so both can read it.
If a parser cannot find contact information, your resume will be dropped from the search results. If the education section is poorly labeled or formatted, a dumb parser will not pick up your degree and you will be dropped from the search results.
NLU based parsers are looking for semantics. Complete sentences are important. A well written project description ranks higher than a skills list. Writing for smart parsers is like writing for recruiters and HR screeners.
The first real person who will read your resume is probably a recruiter or someone in HR. Make their job easy because in many companies, they are the gatekeepers between you and the hiring manager. The first major section in your resume is a capabilities section. This is where you map the connection between your skills and the job requirements. It is a list using plain language.
What these first line screeners are looking for is ability. The feedback they most often get from hiring managers is, “I am looking for hands on experience with, not just knowledge of.” The first section in your resume needs to focus on differentiating yourself from those with textbook knowledge.
If you are a recent graduate or transitioning into the field, this section is the most important in your resume. Regardless of experience, everyone needs to overcome 2 hurdles to get past the HR or recruiter filter.
They are reading to exclude your resume. The reward of spending more than a minute on each resume is low so they speed through, looking for what typically knocks people out of consideration. You need to have each key point covered: education, experience, and applied knowledge.
They have semi-technical knowledge. They are familiar with high level concepts: deep learning, machine vision, python, tensorflow, and statistics. They may not be familiar with decision trees, GANs, transformers, GPU optimization, or project methodologies.
The first section in your resume needs to contain a mixture of language. Introduce each main point with a simplistic explanation using high level terms. Make sure that is the first sentence of each section.