Abstract: A machine learning (ML) process can include teaching, with a teaching set, a first ML algorithm to generate one or more machine-predicted results. One or more weights can be generated based on the one or more machine-predicted results and the teaching set. A second ML algorithm can be generated based on the one or more weights. Via the second ML algorithm, one or more machine-learned results can be generated. A description of one or more candidates can be received. Based on the one or more machine-learned results, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates can be generated. A respective communication can be transmitted to each of a subset of the one or more candidates open to the respective likelihood of interest in the CCG class of positions for the subset above a threshold.

Abstract: A machine learning (ML) process can include teaching, with a teaching set, a first ML algorithm to generate one or more machine-predicted results. One or more weights can be generated based on the one or more machine-predicted results and the teaching set. A second ML algorithm can be generated based on the one or more weights. Via the second ML algorithm, one or more machine-learned results can be generated. A description of one or more candidates can be received. Based on the one or more machine-learned results, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates can be generated. A respective communication can be transmitted to each of a subset of the one or more candidates open to the respective likelihood of interest in the CCG class of positions for the subset above a threshold.

Abstract: Systems and processes for iteratively training a network training module are described herein. In various embodiments, the process includes: (1) retrieving bulk data comprising a plurality of a data types, (2) transforming the bulk data according to preconfigured classification values to generate network information data sets; (3) training a raw training module by iteratively processing each of the network information data sets through a raw training module to generate respective output classification values; (4) updating one or more classification values based on a comparison of the respective output classification values; (5) processing an input network information data set with a trained training module to generate a specific network constituent; and (6) modifying a display based on the plurality of classification values.

Abstract: Systems and processes for iteratively training a training module are described herein. In various embodiments, the process includes: (1) retrieving bulk data comprising a plurality of raw position data elements from a plurality of data sources, (2) transforming the raw position data elements according to preconfigured classification guidelines to generate standardized position data element groups; (3) training a raw training module by iteratively processing each of the standardized position data element groups through a raw training module to generate respective output renumeration values; (4) updating one or more emphasis guidelines based on a comparison of the respective output renumeration values; (5) processing an input position data element set with a trained training module to generate a display renumeration value; and (6) modifying a display based on the display renumeration value.

Abstract: A machine learning system can include a data store and at least one computing device in communication with the data store. The data store can include entity data. The computing device can receive data describing at least one aspect of a position for the entity and generate metadata for the position based on the data describing the at least one aspect, the metadata including skills and tasks associated with the position. The computing device can identify task locations for the entity, determine a distribution of capacity across the task locations based on the entity data, and generate metric scores including a collaboration score, a remote work score, and an estimated remuneration range across each task location. The computing device can generate location scores for each task location based on a weighing of each metric score.

Abstract: A machine learning system can include a data store and a computing device in communication with the data store. The data store can include entity data. The computing device can receive data describing at least one aspect of a position for the entity. The computing device can generate metadata for the position based on the data describing the at least one aspect, the metadata including a plurality of skills and tasks associated with the position. The computing device can identify task locations for the entity and determine a distribution of capacity across the same based on entity data. The computing device can determine physical proximity scores for each skill and task based on the metadata and the corresponding distribution of capacity. The computing device can generate a collaboration score for the position based on the plurality of physical proximity scores.

Abstract: A machine learning system can include a data store and at least one computing device in communication with the data store. The computing device can receive data describing at least one aspect of a position for an entity. The computing device can generate metadata for the position based on the data describing the at least one aspect of the position, the metadata comprising skills and tasks associated with the position. The computing device can identify task locations for the entity and determine a distribution of capacity across the task locations based on entity data describing individuals associated with the entity. The computing device can generate physical proximity scores for each of the skills and tasks based on the metadata for the position, the distribution of capacity, and the task locations. The computing device can generate a remote work score for the position based on the physical proximity scores.

Abstract: A machine learning (ML) process can include teaching, with a teaching set, a first ML algorithm to generate one or more machine-predicted results. One or more weights can be generated based on the one or more machine-predicted results and the teaching set. A second ML algorithm can be generated based on the one or more weights. Via the second ML algorithm, one or more machine-learned results can be generated. A description of one or more candidates can be received. Based on the one or more machine-learned results, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates can be generated. A respective communication can be transmitted to each of a subset of the one or more candidates open to the respective likelihood of interest in the CCG class of positions for the subset above a threshold.