Performance-based hires versus resume-based hires
Take a look at some typical job descriptions which have flooded in Indeed or Monster and you will see a catalog of required skills, coding languages, and experiences. Take a look at the pile of resumes beside you and try matching the key words of each resume with the requirements in the job descriptions to see how many pass...
Stop for a moment think: How do you know that the list of skills are really required by this position? How do you know that those candidates really have the skills listed in their resumes?
Through data, we found that in many cases the skills listed in the job description are outdated and unnecessary (hiring a web developer that knows ColdFusion in 2018, for instance), and the more skills that candidates list on their resume, the less likely they turn out to be talented. When you are hiring an engineer, data scientist, etc., what you are looking for is whether they will perform well solving a problem in a certain environment with specific languages. Filtered now supports 30+ languages to evaluate candidates.
Reducing unconscious bias is incredibly important when screening for the best candidates. During review unconsciously-biased reviewers may associate some things about their candidate with a feeling of good or bad and thus apply different standards to each. Valuable talent can be easily lost during this process.
By starting with an objective score instead of a resume, Filtered creates an environment where you can compare candidates fairly. If requested, it is possible to turn off tabs tracking, hide the avatar, hide the resume, and even mask names to reduce unconscious bias further.
Filtered runs all candidates’ codes against test cases. The output from the candidate’s code is compared with expected output to determine whether the case has passed or failed. Test cases are many different sizes and cover many different levels of complexity. Furthermore, coding time, execution time, memory usage, and code quality are also rated. Clients will get a leaderboard of all the candidates and their scores (best-in, first-out).
Another common scenario is to use Filtered to benchmark the internal tech team and evaluate their performance BEFORE hiring. This is especially valuable when trying to fill skill gaps in your organization.