Abstract: Methods, systems, and apparatuses for a background check data co-op platform. A method includes receiving, from a verified, trusted third party (VTTP) computing system, validation data associated with a qualification included in a digital work wallet of a job seeker, and determining, based on the qualification, that the qualification is a static qualification. The method further includes, based on the determination that the qualification is a static qualification, storing, in the digital work wallet based on the validation data, an indication that the qualification is validated. The method further includes, based on receipt of a request to submit an application for a job by the job seeker, sending the digital work wallet with the indication that the qualification is validated to an employer system associated with the job.

Abstract: Methods, systems, and apparatuses for a career management co-op platform. A method includes receiving, from a device associated with a prospective job candidate, a request for consideration for a first job type for the job candidate; and determining, based on aggregated job candidate data of a plurality of job candidates having the first job type, one or more relevant skills possessed by the plurality job candidates. The method further includes determining, via a machine learning inference based on a comparison of current skills possessed by the prospective job candidate and the relevant skills, a job seeker skillset match score; and based on the skillset match score satisfying a threshold, providing, to the device, a recommendation of a second job type relevant to at least one of the one or more relevant skills missing from the current skills of the prospective job candidate.

Abstract: A method and a system are disclosed for providing an Artificial Intelligence (AI) based recruitment management, the method comprising. An AI-based tracker module having a graphical user interface is configured for receiving a plurality of job applications from a plurality of candidates via respective user devices. The tracker is configured to predict the hiring probability of a candidate based on: a compensation viability score, a speed score, a skill sets of the candidate. The tracker is further configured to predict time to fill a position by the candidate, based on one or more parameters including location, salary, and skill set.