Abstract: Aspects map values of skills data for candidates to skills metadata representations stored within a metadata repository that includes skills metadata representation data dimensions for other candidates; filter via machine learning a top-trending subset of job classifications that have better career opportunity values from a universe of job classifications defined within the repository dimensional data values; determine via machine learning career path viability values for the top-trending subset job classifications as a function of strength of match to candidate dimensional values; project likely future values of mapped candidate values at the end of a future time period within a simulated work market scenario; and prioritize the top-trending subset job classifications as potential career paths for candidates as a function of the career path viability values and the projected future values of the dimensional data mapped for the candidates within the repository.

Abstract: A system includes one or more processors to identify a first plurality of attributes associated with a first opening and an entity structure, identify a second plurality of attributes associated with a plurality of positions and one or more entity structures, identify a target item missing from a metadata representation of a candidate for a second opening within the entity structure, execute an automated interview process for the candidate, generate an updated metadata representation of the candidate based on a value for the target item, and provide data of the candidate for display via an interface of a device of the entity structure.

Abstract: Aspects identify target dimensional data value items via machine learning that are most strongly correlated to successful hires for job opportunities within employment data that are similar to a new job opportunity. In response to determining that the target item value for a candidate is deficient to qualify for the new job opportunity, aspects engage the candidate in an automated artificial intelligence chat bot agent interview process that acquires interview audio and image response data from the candidate; extract data relevant to the target item from interview audio and image data; determine an objective value for the target item as a function of the extracted data; and qualify the candidate for suitability for the new job opportunity as a function of resume data mapped to the metadata representation of the candidate and the objective value determined for the target item.

Abstract: Aspects map values of skills data for candidates to skills metadata representations stored within a metadata repository that includes skills metadata representation data dimensions for other candidates; filter via machine learning a top-trending subset of job classifications that have better career opportunity values from a universe of job classifications defined within the repository dimensional data values; determine via machine learning career path viability values for the top-trending subset job classifications as a function of strength of match to candidate dimensional values; project likely future values of mapped candidate values at the end of a future time period within a simulated work market scenario; and prioritize the top-trending subset job classifications as potential career paths for candidates as a function of the career path viability values and the projected future values of the dimensional data mapped for the candidates within the repository.

Abstract: Aspects identify target dimensional data value items via machine learning that are most strongly correlated to successful hires for job opportunities within employment data that are similar to a new job opportunity. In response to determining that the target item value for a candidate is deficient to qualify for the new job opportunity, aspects engage the candidate in an automated artificial intelligence chat bot agent interview process that acquires interview audio and image response data from the candidate; extract data relevant to the target item from interview audio and image data; determine an objective value for the target item as a function of the extracted data; and qualify the candidate for suitability for the new job opportunity as a function of resume data mapped to the metadata representation of the candidate and the objective value determined for the target item.

Abstract: Aspects map, without association to job description data, candidate skills and activity data values to a metadata representation within a metadata repository; determine, without association to the job description data, via a machine learning process, a plurality of employability values for the candidate for top-trending jobs as a function of strength of match of the mapped activity and skills values to respective skills and activity data values that are associated within the repository to top-trending jobs without association to values of the job description data that are associated to the top trending jobs; generate a prioritized subset of the top trending jobs that omits jobs that have employability values failing to meet a minimum threshold employability value; and drive a graphical user interface display to present the prioritized subset of the top trending jobs to the candidate ranked as a function of their determined employability values.

Abstract: Aspects map candidate resume data values to a resume metadata representation of the candidate defined by data dimensions stored within a metadata repository that includes resume metadata representation data dimensions of a plurality of candidates; learn via a machine learning process different trending demand values for job classifications within the dimensional data as a function of employment data; identify via the machine learning process an upwardly trending job position skill missing from the candidate data dimensions and most likely to match a current skill set of the candidate; add the identified skill to the first candidate data dimensions; and generate a resume for the first candidate as a function of the first candidate data dimensions to include the added skill.