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 supports 30+ languages for evaluations. Our goal is to make sure our tests are deep enough to cover multiple skill levels within each engineering discipline.
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’ code against test cases. The output from the candidate’s code is compared with the 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 its performance BEFORE hiring. This is especially valuable when trying to fill skill gaps in your organization.