MACHINE LEARNING SYSTEMS FOR REMOTE ROLE EVALUATION AND METHODS FOR USING SAME

1 . A machine learning system, comprising:

a data store comprising entity data for an entity;

at least one computing device in communication with the data store, the at least one computing device being configured to:

receive data describing at least one aspect of a position for the entity;

generate metadata for the position based on the data describing the at least one aspect of the position, the metadata comprising a plurality of skills and tasks associated with the position;

identify a plurality of task locations for the entity;

determine a distribution of capacity across the plurality of task locations based on the entity data;

generate a plurality of physical proximity scores for each of the plurality of skills and tasks based on the metadata for the position, the distribution of capacity, and the plurality of task locations; and

generate a remote work score for the position based on the plurality of physical proximity scores.

2 . The machine learning system of claim 1 , wherein generating the plurality of physical proximity scores is further based on at least one data set comprising a plurality of known proximity scores corresponding to a plurality of known skills and tasks.

3 . The machine learning system of claim 1 , wherein the at least one computing device is further configured to:

determine the remote work score is below a predefined remote work threshold; and

identify at least one particular physical location for the position based on the plurality of physical proximity scores.

4 . The machine learning system of claim 1 , wherein the metadata further comprising at least one of: a location of the position, an identifier of the entity, and a position identifier.

5 . The machine learning system of claim 1 , wherein the at least one computing device is further configured to generate the metadata by parsing the data describing the at least one aspect of the position by applying a deep learning and natural language processing algorithm to the at least one aspect of the position.

6 . The machine learning system of claim 1 , wherein the at least one computing device is further configured to periodically capture and index publically accessible data relating to positions and store the data in the data store for use in generating the metadata for the position.

7 . A machine learning method, comprising:

receiving, via at least one computing device, data describing at least one aspect of a position for an entity;

receiving, via the at least one computing device, entity data corresponding to the entity;

generating, via the at least one computing device, metadata for the position based on the data describing the at least one aspect of the position, the metadata comprising a plurality of skills and tasks associated with the position;

identifying, via the at least one computing device, a plurality of task locations for the entity;

determining, via the at least one computing device, a distribution of capacity across the plurality of task locations based on the entity data;

generating, via the at least one computing device, a plurality of physical proximity scores for each of the plurality of skills and tasks based on the metadata for the position, the distribution of capacity, and the plurality of task locations; and

generating, via the at least one computing device, a remote work score for the position based on the plurality of physical proximity scores.

8 . The machine learning method of claim 7 , wherein the metadata is generated further based on the entity data.

9 . The machine learning method of claim 7 , further comprising generating a remote working designation for the position based on the remote work score falling into a particular bin of a plurality of remote work score bins.

10 . The machine learning method of claim 7 , further comprising:

generating, via the at least one computing device, a training data set for the entity using the entity data, the training data set comprising first position data describing known on-premise positions and second portion data describing known remote positions; and

training, via the at least one computing device, a machine learning model using the training data set, wherein the plurality of physical proximity scores are generated via the machine learning model.

11 . The machine learning method of claim 10 , wherein the machine learning model is configured to identify remote working criteria predictive for on-premise or remote positions when executed by the at least one computing device, wherein the method further comprises:

generating, via the at least one computing device, a second machine learning model based on the machine learning model, the second machine learning model being configured to predict a proximate importance for each the plurality of skills and tasks using the remote working criteria.

12 . The machine learning method of claim 10 , further comprising:

receiving, via the at least one computing device, a remote work result for the position subsequent to fulfilment of the position; and

retraining, via the at least one computing device, the machine learning model based on the remote work result.

13 . A non-transitory computer-readable medium embodying a program that, when executed by at least one computing device, causes the at least one computing device to:

receive data describing at least one aspect of a position for an entity;

generate metadata for the position based on the data describing the at least one aspect of the position, the metadata comprising a plurality of skills and tasks associated with the position;

identify a plurality of task locations for the entity;

determine a distribution of capacity across the plurality of task locations based on entity data corresponding to the entity;

generate a plurality of physical proximity scores for each of the plurality of skills and tasks based on the metadata for the position, the distribution of capacity, and the plurality of task locations; and

generate a remote work score for the position based on the plurality of physical proximity scores.

14 . The non-transitory computer-readable medium of claim 13 , wherein the program further causes the at least one computing device to generate the remote work score by combining the plurality of physical proximity scores for each of the plurality of skills and tasks according to a predetermined weighting.

15 . The non-transitory computer-readable medium of claim 14 , wherein the program further causes the at least one computing device to compute the predetermined weighting for combining the plurality of physical proximity scores based on the metadata.

16 . The non-transitory computer-readable medium of claim 13 , wherein the program further causes the at least one computing device to generate the remote work score via a trained machine learning model.

17 . The non-transitory computer-readable medium of claim 16 , wherein the program further causes the at least one computing device to generate the trained machine learning model by:

generating an initial machine learning model;

training, with a training dataset, the initial machine learning model to generate one or more experimental remote work predictions, wherein the training dataset comprises historical entity data associated with the position and one or more known remote work outcomes associated with the historical entity data;

determining an error of the initial machine learning model by comparing the one or more experimental remote work predictions to the one or more known remote work outcomes; and

generating a secondary machine learning model by adjusting the initial machine learning model based on the error, wherein the trained machine learning model is the secondary machine learning model.

18 . The non-transitory computer-readable medium of claim 17 , wherein:

the initial machine learning model comprises a plurality of parameters and a first set of weight values that are applied to each of the plurality of parameters, wherein:

the plurality of parameters are based on the plurality of physical proximity scores; and

the first set of weight values determines a level of contribution of each of the plurality of parameters to the remote work score; and

the program further causes the at least one computing device to generate the secondary machine learning model by:

determining at least one of the plurality of parameters that most contributed to the error;

adjusting one or more weight values of the first set of weight values that are associated with the at least one the plurality of parameters to generate a secondary set of weight values; and

generating the secondary machine learning model based on the plurality of parameters and the secondary set of weight values.

19 . The non-transitory computer-readable medium of claim 13 , wherein the program further causes the at least one computing device to generate a report comprising the remote work score.

20 . The non-transitory computer-readable medium of claim 19 , wherein:

the program further causes the at least one computing device to determine at least one physical proximity score from the plurality of physical proximity scores that most positively contributed to the remote work score; and

the report further comprises the at least one physical proximity score that most positively contributed to the remote work score.