System and method for recruitment candidate equity modeling

Non-transitory computer storage that stores executable program instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising:

• registering a plurality of candidates in a candidate database;
• generating a profile for each candidate and storing the profiles in the candidate database;
• submitting a candidate to one or more job positions, based on the candidate profile, wherein the one or more job positions are associated with one or more clients;
• assigning a recruitment stage parameter to each submitted candidate, based on state of the candidate in a recruiting pipeline, wherein the recruiting pipeline comprises a plurality of recruitment stages;
• for each recruitment stage, computing a transition percentage as the ratio between the number of candidates reaching the next stage and the number of candidates reaching the current stage;
• storing the assigned recruitment stage parameter in an equity model database;
• storing in the equity model database an identifier of a recruiter associated with the candidate, obtained from a recruiter database;
• tracking, in the equity model database, a plurality of candidates in the recruiting pipeline, wherein tracking comprises updating the assigned recruitment stage parameter for each submitted candidate in the equity model database, based on state of the candidate in the recruiting pipeline;
• for each candidate, generating in an analytics module comprising a machine learning model, a transition likelihood, comprising a likelihood of the candidate transitioning between stages of the recruiting pipeline, wherein the transition likelihood is generated, at least in part, based on the transition percentage and an amount of time the candidate has spent in a stage of the pipeline, wherein the transition likelihood decreases exponentially by a given percentage each day, the percentage calculated from historical data including the median and standard deviation of past transition times;
• for each candidate, generating by the machine learning model, a placement likelihood corresponding to likelihood that the candidate will be placed in the position based on inputting to the machine learning model a plurality of features including a current stage of the candidate in the recruiting pipeline, wherein the machine learning model is trained based on a dataset of training examples each comprising the plurality of features and a placement label, the placement label comprising an indication of whether a corresponding candidate was placed;
• for each candidate, adjusting the placement likelihood by a depreciation amount comprising a staleness parameter, the staleness parameter determined by the length of time since the last change in the candidate's stage;
• for each candidate, generating in the analytics module, a candidate equity based on the placement likelihood and an expected placement value associated with the client;
• and
• calculating the difference between the candidate equity at a first time and the candidate equity at a second time.