Gabi’s AI scores resumes based on the role’s requirements and gives each resume a score based on:
Profile match
Gabi scans the candidates' profiles to find keywords in their resumes that match the keywords in the job description. The more matches Gabi finds the higher the profile match score assigned to the resume.
For example, a requisition for a graphic design role will have keywords such as Photoshop, InDesign, conceptual thinking etc. If a candidate with a background in painting were to apply for a such a position, they might have some keywords that match the requisition but they will be ranked lower than a candidate with a multi-media design background, who will have a lot more of the necessary skills and experience necessary for the role in question. This allows Gabi to eliminate unfit resumes quickly and give you a better selection.
Job Title Similarity
Gabi will look for similarities between the job title advertised in the requisition and the ones mentioned in the resume. A candidate with the right experience will have more hits on job title similarities.
For example, a candidate who has worked as an Infrastructure Engineer, will score well on a job requisition for a DevOps Engineer.
Scanning for job title similarities helps Gabi rank candidates with the right experience higher up the shortlist.
Skill Strength
Every role has certain skills that are more valuable than others. For example, an architect’s primary skill is designing structures. His skills at landscaping are secondary. Gabi can recognize those skills which are the most relevant for any given role and rank higher resumes that mention those skills.
After assigning a score to each of these categories, Gabi will add them up to present each resume with a final score out of a 100. The candidates will then be ranked and shortlisted on the basis of this final score.
