12. A method comprising: selecting, by a model-driven candidate-sorting tool executing on a processing device, a data set of digital interview data for model-driven candidate sorting, wherein the data set comprises candidate data recorded for a plurality of interviewing candidates, wherein the candidate data comprises voice-to-text data; for the plurality of interviewing candidates, analyzing, by the model-driven candidate-sorting tool, the candidate data for the respective interviewing candidate to identify a plurality of digital interviewing cues, including audio cues, wherein analyzing the candidate data comprises generating, by an audio cue generator of the model-drive candidate sorting tool, the audio cues based on the voice-to-text data for the respective interviewing candidate; and applying the digital interviewing cues to a prediction model to predict an achievement index based on the plurality of digital interviewing cues identified for the respective interviewing candidate, wherein the applying the plurality of digital interviewing cues to the prediction model to predict the achievement index is performed without reviewer input at the model-driven candidate-sorting tool; sorting a list of the plurality of interviewing candidates based on the predicted achievement indices from the applying the plurality of digital interviewing cues to the prediction model; and presenting the sorted list of the plurality of interviewing candidates in a user interface to a reviewer.
12. A method comprising: selecting, by a model-driven candidate-sorting tool executing on a processing device, a data set of digital interview data for model-driven candidate sorting, wherein the data set comprises candidate data recorded for a plurality of interviewing candidates, wherein the candidate data comprises voice-to-text data; for the plurality of interviewing candidates, analyzing, by the model-driven candidate-sorting tool, the candidate data for the respective interviewing candidate to identify a plurality of digital interviewing cues, including audio cues, wherein analyzing the candidate data comprises generating, by an audio cue generator of the model-drive candidate sorting tool, the audio cues based on the voice-to-text data for the respective interviewing candidate; and applying the digital interviewing cues to a prediction model to predict an achievement index based on the plurality of digital interviewing cues identified for the respective interviewing candidate, wherein the applying the plurality of digital interviewing cues to the prediction model to predict the achievement index is performed without reviewer input at the model-driven candidate-sorting tool; sorting a list of the plurality of interviewing candidates based on the predicted achievement indices from the applying the plurality of digital interviewing cues to the prediction model; and presenting the sorted list of the plurality of interviewing candidates in a user interface to a reviewer. 16. The method of claim 12 , wherein the candidate data comprises timing data, and wherein analyzing the candidate data for the respective interviewing candidate comprises at least one of: determining a time metric representative of the respective interviewing candidate's timeliness on starting and completing an interview; determining whether the respective interviewing candidate missed a deadline; determining whether the respective interviewing candidate requested additional time to complete the interview, and wherein the method further comprises generating a digital interview cue of the plurality of digital interview cues based on at least one of the time metric, whether the respective interviewing candidate missed the deadline or whether the respective interviewing candidate requested additional time to complete the interview.