Job applicant probability of confirmed hire

1. A computer system, comprising:

a processor;

a storage device;

a candidate predictor engine; and

a memory device holding an instruction set executable on the processor to cause the computer system to perform operations comprising:

training a machine learning model to encode an applicant quality (AQ) score for a job/applicant pair based on input features comprising job features associated with the job of the job/applicant pair, and member profile features associated with a member profile of the applicant of the job/applicant pair;

encoding, by the candidate predictor engine using the trained model, data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting;

storing, on the storage device, the encoded data representing the AQ scores for each job/applicant pair for the plurality of applicants to the job posting;

assigning member-level weights to each of the plurality of applicants;

calculating, by the candidate predictor engine, weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores retrieved from the storage device and member-level weights;

summing the weighted AQ scores to derive a total weighted score for the job posting;

generating, by the candidate predictor engine, a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the job posting being filled by an applicant; and

transmitting the job-level pCH to a client device for display.

2. The computer system of claim 1 , wherein assigning member-level weights to each of the plurality of applicants comprises:

retrieving, from the storage device, the respective AQ scores of the plurality of applicants; and

assigning greater weights to applicants with higher AQ scores.

3. The computer system of claim 1 , wherein the AQ scores are encoded by the candidate predictor engine using the trained model based in part on at least one pre-defined type of candidate feature retrieved from a job candidate context feature set stored on the storage device, the candidate feature comprising at least one of a job function descriptor represented in a social network service profile of a target candidate account, a job industry descriptor represented in the social network service profile of the target candidate account, and a job company size descriptor represented in the social network service profile of the target candidate account.

4. The computer system of claim 3 , wherein encoding the data representing the AQ score for each job/applicant pair comprises encoding a candidate-to-job comparison feature data subset to include a difference value that corresponds with a pre-defined type of comparison feature that is learned as being predictive of whether a given job candidate is qualified for a given job posting, encoding the candidate-to-job comparison feature data subset comprising:

identifying years of candidate professional experience based on at least one employment time period descriptor in a social network service profile of the target candidate account;

identifying years of required professional experience described in the job posting;

calculating a years difference value to represent a difference between the years of candidate professional experience and the years of required professional experience;

inserting the years difference value into the candidate-to-job comparison feature data subset as a respective comparison feature; and

storing the candidate-to-job comparison feature data subset on the storage device.

5. The computer system of claim 4 , wherein inserting the years difference value into the candidate-to-job comparison feature data subset as the respective comparison feature comprises:

encoding the years difference value into a pre-defined data position for a years difference comparison feature in the candidate-to-job comparison feature data subset; and

storing the candidate-to-job comparison feature data subset on the storage device.

6. The computer system of claim 3 , wherein encoding the data representing the AQ score for each job/applicant pair comprises encoding a candidate-to-job comparison feature data subset to include a difference value that corresponds with a pre-defined type of comparison feature that is learned as being predictive of whether a given job candidate is qualified for a given job posting, encoding the candidate-to-job comparison feature data subset comprising:

identifying each skill descriptor in a social network service profile of the target candidate account;

identifying each skill descriptor described in the job posting;

calculating a skill match value to represent a percentage of matching skill descriptors between the target candidate account's skill descriptors and the job posting's skill descriptors;

inserting the skill match value into the candidate-to-job comparison feature data subset as a respective comparison feature; and

storing the candidate-to-job comparison feature data subset on the storage device.

7. The computer system of claim 6 , wherein each skill descriptor in the social network service profile of the target candidate account comprises a respective skill descriptor selected by the target candidate account.

8. The computer system of claim 6 , wherein each skill descriptor in the social network service profile of the target candidate account comprises a respective skill descriptor selected by a member account having a social network connection with the target candidate account.

9. The computer system of claim 6 , wherein inserting the skill match value into the candidate-to-job comparison feature data subset as a respective comparison feature comprises:

encoding the skill match value into a pre-defined data position for a skill match comparison feature in the candidate-to-job comparison feature data subset; and

storing the candidate-to-job comparison feature data subset on the storage device.

10. The computer system of claim 1 , wherein the candidate predictor engine comprises a prediction generator, and wherein generating the job-level pCH further comprises generating, by the prediction generator, a prediction output based on job-level characteristics including one or more of a channel, an application path, and an organization size, the prediction output indicating whether applicants of the plurality of applicants are qualified for the job posting.

11. The computer system of claim 10 , wherein the job-level characteristics include at least one pre-defined type of job feature comprising at least one of a job function descriptor represented in the job posting, a job industry descriptor represented in the job posting, and a job company size descriptor represented in the job posting.

12. The computer system of claim 1 , wherein the operations further comprise instantiating a plurality of job candidate decision trees, each job candidate decision tree comprising at least one learned decision tree branch label, instantiating the plurality of job candidate decision trees comprising:

collecting, from within a social network service, logged interaction data comprising at least a first previous determination, by a first employer account, of a first candidate account as qualified for a first job posting, and a second previous determination, by a second employer account, of a second candidate account as not qualified for a second job posting.

13. The computer system of claim 12 , wherein instantiating the plurality of job candidate decision trees, each job candidate decision tree comprising at least one learned decision tree branch label, further comprises:

executing a random forest process with the logged interaction data to build each job candidate decision tree by learning a branch label for each branch of each job candidate decision tree based on at least one attribute of the logged interaction data.

14. A computer-implemented method, comprising:

training a machine learning model to encode an applicant quality (AQ) score for a job/applicant pair based on input features comprising job features associated with the job of the job/applicant pair, and member profile features associated with a member profile of the applicant of the job/applicant pair;

encoding, using the trained model, data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting;

assigning member-level weights to each of the plurality of applicants;

calculating weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights;

summing the weighted AQ scores to derive a total weighted score for the job posting;

generating a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the job posting being filled by an applicant; and

transmitting the job-level pCH to a client device for display.

15. The computer-implemented method of claim 14 , wherein encoding the data representing the AQ score for each job/applicant pair comprises encoding data representing a job candidate context feature set based on at least one attribute of a target candidate account of a social network service and at least one attribute of a job posting on the social network service, encoding the data representing the job candidate context feature set comprising:

encoding a job candidate feature data subset to include the at least one attribute of the target candidate account that corresponds with at least one pre-defined type of candidate feature that is learned as being predictive of whether a given job candidate is qualified for a given job posting;

encoding a job feature data subset to include the at least one attribute of the job posting that corresponds with at least one pre-defined type of job feature that is learned as being predictive of whether the given job candidate is qualified for the given job posting; and

encoding a candidate-to-job comparison feature data subset to include a difference value between the at least one attribute of the target candidate account and the at least one attribute of the job posting that corresponds with a pre-defined type of comparison feature that is learned as being predictive of whether the given job candidate is qualified for the given job posting.

16. The computer-implemented method of claim 15 , wherein encoding the candidate-to-job comparison feature data subset to include the difference value that corresponds with the pre-defined type of comparison feature that is learned as being predictive of whether the given job candidate is qualified for the given job posting comprises:

identifying years of candidate professional experience based on at least one employment time period descriptor in a social network service profile of the target candidate account;

identifying years of required professional experience described in the job posting on the social network service;

calculating a years difference value to represent a difference between the years of candidate professional experience and the years of required professional experience; and

inserting the years difference value into the candidate-to-job comparison feature data subset as a respective comparison feature.

17. The computer-implemented method of claim 16 , wherein inserting the years difference value into the candidate-to-job comparison feature data subset as a respective comparison feature comprises:

encoding the years difference value into a pre-defined data position for a years difference comparison feature in the candidate-to-job comparison feature data subset.

18. The computer-implemented method of claim 15 , wherein encoding the candidate-to-job comparison feature data subset to include the difference value that corresponds with the pre-defined type of comparison feature that is learned as being predictive of whether the given job candidate is qualified for the given job posting comprises:

identifying each skill descriptor in a social network service profile of the target candidate account;

identifying each skill descriptor described in the job posting on the social network service;

calculating a skill match value to represent a percentage of matching skill descriptors between the target candidate account's skill descriptors and the job posting's skill descriptors; and

inserting the skill match value into the candidate-to-job comparison feature data subset as a respective comparison feature.

19. The computer system of claim 18 , wherein each skill descriptor in the social network service profile of the target candidate account comprises a respective skill descriptor selected by the target candidate account.

20. A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including:

training a machine learning model to encode an applicant quality (AQ) score for a job/applicant pair based on input features comprising job features associated with the job of the job/applicant pair, and member profile features associated with a member profile of the applicant of the job/applicant pair;

Encoding, using the trained model, data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting, the AQ score being based on an AQ model;

assigning member-level weights to each of the plurality of applicants;

calculating weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights;

summing the weighted AQ scores to derive a total weighted score for the job posting;

generating a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the job posting being filled by an applicant; and

transmitting the job-level pCH to a client device for display.