Abstract: Cross-sectional scaling of electronically available occupational profile data to represent a workforce of individuals at one or more companies is described. Raw online occupational profile data does not accurately represent a company's workforce. Individuals in certain roles have a higher or lower likelihood of being represented electronically, and individuals in certain regions also have higher or lower likelihoods of being represented. Thus, simply aggregating available online data does not produce an accurate representation of a company's workforce. To overcome this and/or other problems, cross-sectional scaling techniques are used to output an accurate representation of the workforce at one or more companies (e.g., for one or more occupations at a given company) based on the likelihoods that individuals have electronically available occupational profile data (which are determined based on determined occupational groups and regions where the one or more companies operate).

Abstract: Systems and methods for providing a universal occupational taxonomy establish one or more levels of granularity of a plurality of jobs; assemble data for each job of the plurality of jobs; train one or more vector representations, in which a vector representation is trained for each job of the plurality of jobs; reevaluate the one or more levels of granularity based on the training; cluster the plurality of jobs into one or more clusters based on the one or more vector representations; name the one or more resulting clusters a representative title; classify the one or more jobs to the one or more clusters; and output an occupational taxonomy.

Abstract: Systems and methods for providing a universal occupational taxonomy establish one or more levels of granularity of a plurality of jobs; assemble data for each job of the plurality of jobs; train one or more vector representations, in which a vector representation is trained for each job of the plurality of jobs; reevaluate the one or more levels of granularity based on the training; cluster the plurality of jobs into one or more clusters based on the one or more vector representations; name the one or more resulting clusters a representative title; classify the one or more jobs to the one or more clusters; and output an occupational taxonomy.

Abstract: Embodiments for managing skills as a cluster using machine learning and a domain knowledge expert by a processor. An adjacency of one or more target skills and one or more skills of each of a plurality of entities may be determined. The adjacency of skills may be used to generate one or more skill clusters. One or more domain knowledge experts may be used to correct the one or more skill clusters. The skill clusters corrected by the domain knowledge experts may be used to correct the skill adjacencies. The corrected skill adjacencies may be used to select candidates for reskilling. A skill demand of the one or more skill clusters may be forecasted.

Abstract: Embodiments for estimating substitutability between skills by combining skill similarities from one or more data sources by a processor. An adjacency of skill similarity of one or more skills of one or more entities may be determined. The adjacency of skill similarity may be used to generate one or more skill clusters. Skill demand of the one or more skill clusters may be forecasted.