10. A computer-based method for prioritizing a list of job candidates for a job opening with a firm, the method comprising: using a Random Forest Algorithm to generate, by a computer system that comprises at least one processor and at least one memory unit, a quantity T decision trees, where T is greater than or equal to 2, and the T decision trees are trained based on a training data set of historical candidates classified hired or unhired classes, wherein each decision tree of the T decision trees is generated by selecting historical candidates in the training dataset randomly with replacement to train the decision tree; and for each of the job candidates, determining, by the computer system, a classification of the job candidate for each of the T decision trees; computing, by the computer system, a hiring probability for each job candidate based on a ratio of (i) a total of number of times the job candidate was classified as hired to (ii) T; and ranking, by the computer system, the job candidates by hiring probability to generate a prioritized list of the job candidates.
12. The method of claim 10 , wherein: there are N historical candidates in the training data set; and selecting, for the training of each of the T decision trees, comprises selecting N historical candidates in the training dataset randomly with replacement.