Machine learning systems and methods for predictive engagement

1. A machine learning process comprising:

training, with a primary training set, a first machine learning algorithm to identify a difference between known contract, contingent, or gig (CCG)-based position holders and known non-CCG-based position holder;

identifying, via the first machine learning algorithm, a parameter based on the differences identified;

generating, via the first machine learning algorithm, a weight for the identified parameter such that the weight corresponds to a positive or negative indication of a likelihood that a first candidate is a CCG-based position holder;

generating a second machine learning algorithm based on the weight generated by the first learning algorithm to predict a likelihood of a second candidate as being a CCG-based position holder;

generating a secondary training set including known CCG-based position holders and known non-CCG-based position holders;

generating, via the at least one second machine learning algorithm, one or more machine-learned results;

improving accuracy of the second machine learning algorithm with the secondary training set by reconfiguring the second machine learning algorithm to reduce an error metric based on a comparison between the one or more machine-learned results and known results;

receiving a description of one or more candidates;

generating, via the at least one second machine learning algorithm, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates; and

generating a respective communication 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.

2. The machine learning process of claim 1 , further comprising:

receiving a set of candidate parameters for a particular position, the particular position corresponding to the CCG class of positions; and

processing the set of candidate parameters to identify one or more candidates from a set of candidates that meet the set of candidate parameters.

3. The machine learning process of claim 1 , further comprising:

generating a ranking of the subset of the one or more candidates based on the respective likelihood of interest in the CCG class of positions; and

generating a communication based on the ranking of the subset.

4. The machine learning process of claim 1 , further comprising:

generating particular language designed to provoke a response from each of the subset of the one or more candidates; and

generating one or more strings of text via natural language processing for the respective communication for each of the subset of the one or more candidates, wherein the one or more strings of text comprise language are based on the particular language.

5. The machine learning process of claim 1 , wherein the primary training set describes one or more first communications with a known positive result and one or more second communications with a known negative result.

6. The machine learning process of claim 1 , further comprising:

receiving an indication that a particular candidate of the one or more candidates does not prefer the CCG class of positions;

subsequent to receiving the indication, generating a change in a profile associated with the particular candidate;

generating that the change in the profile increases a likelihood of interest in the CCG class of positions more than or equal to a threshold amount; and

in response to the change increasing the likelihood of interest more than or equal to the threshold amount, adjusting the profile to facilitate communication with the particular candidate.

7. The machine learning process of claim 1 , wherein the description of the one or more candidates is extracted from at least one of media and investigative information.

8. A machine learning system comprising:

memory comprising a primary training set describing one or more first communications with a known positive result and one or more second communications with a known negative result; and

at least one device in communication with the memory, the at least one device being configured to:

train, with a primary training set, at least one first machine learning algorithm to identify a difference between known contract, contingent, or gig (CCG)-based position holders and known non-CCG-based position holder;

identify, via the at least one first machine learning algorithm, a parameter based on the differences identified;

analyze, via the at least one first machine learning algorithm, a weight for the identified parameter such that the weight corresponds to a positive or negative indication of a likelihood that a first candidate is a CCG-based position holder;

generate at least one second machine learning algorithm based on the weight analyzed by the first learning algorithm predict a likelihood of a second candidate as being a CCG-based position holder;

generate a secondary training set including known CCG-based position holders and known non-CCG-based position holders;

generate, via the at least one second machine learning algorithm, one or more machine-learned results;

improve accuracy of the at least one second machine learning algorithm with the secondary training set by reconfiguring the at least one second machine learning algorithm to reduce an error metric based on a comparison between the one or more machine-learned results and known results; and

analyze, via the at least one second machine learning algorithm, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates.

9. The machine learning system of claim 8 , wherein the at least one device is further configured to exclude any candidates from the one or more candidates that does not meet a predefined threshold.

10. The machine learning system of claim 8 , wherein the at least one device is further configured to generate a respective communication 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.

11. The machine learning system of claim 10 , wherein the at least one device is further configured to:

analyze a respective result associated with the respective communication for each of the subset of the one or more candidates; and

transform the primary training set based on the respective result for each of the subset of the one or more candidates.

12. A machine learning system comprising:

memory; and

at least one device in communication with the memory, the at least one device being configured to:

train, with a primary training set, at least one first machine learning algorithm to identify a difference between known contract, contingent, or gig (CCG)-based position holders and known non-CCG-based position holder;

analyze via the at least one first machine learning algorithm, a weight for the identified parameter such that the weight corresponds to a positive or negative indication of a likelihood that a first candidate is a CCG-based position holder;

generate at least one second machine learning algorithm based on the weight analyzed by the first learning algorithm to predict a likelihood of a second candidate as being a CCG-based position holder;

generate a secondary training set including known CCG-based position holders and known non-CCG-based position holders;

generate, via the at least one second machine learning algorithm, one or more machine-learned results;

improve accuracy of the at least one second machine learning algorithm with the secondary training set by reconfiguring the at least one second machine learning algorithm to reduce an error metric based on a comparison between the one or more machine-learned results and known results;

analyze, via the at least one second machine learning algorithm, a respective likelihood of interest in a CCG class of positions for each of one or more candidates; and

generate a respective communication 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.

13. The machine learning system of claim 12 , wherein the at least one device is further configured to:

analyze a respective result associated with the respective communication for each of the subset of the one or more candidates; and

transform the primary training set based on the respective result for each of the subset of the one or more candidates.

14. The machine learning system of claim 13 , wherein the at least one device is further configured to:

transform, with the transformed primary training set, the at least one first machine learning algorithm to generate a transformed one or more machine predicted results; and

analyze one or more transformed weights based on the transformed one or more machine predicted results and the transformed teaching set.

15. The machine learning system of claim 14 , wherein the at least one device is further configured to:

generate at least one transformed second machine learning algorithm based on the one or more transformed weights;

generate, via the at least one transformed second machine learning algorithm, one or more additional machine-learned results; and

identify a transformed respective likelihood of interest in the CCG class of positions for each of the one or more candidates based on the one or more additional machine-learned results.

16. The machine learning system of claim 12 , wherein the at least one device is further configured to:

receive a set of candidate parameters for a particular position, the particular position corresponding to the CCG class of positions; and

process the set of candidate parameters to identify a candidate subset of the one or more candidates that meets the set of candidate parameters, wherein the subset of the one or more candidates are selected from the candidate subset.

17. The machine learning system of claim 12 , wherein the at least one device is further configured to:

receive the respective likelihood of interest in the CCG class of positions for the one or more candidates;

analyze, for each candidate, if the respective likelihood of interest for a subset of the one or more candidates meets a threshold for a particular position; and

generate and transmit, to a profile associated with the particular position, a description of the subset of the one or more candidates that meet the threshold.

18. The machine learning system of claim 12 , wherein the at least one device is further configured to exclude any candidates from the one or more candidates that does not meet a predefined threshold.