Methods and systems for a job suggestions engine and talent timeline in a talent management application

A job suggestion system, comprising:

• a memory storing a plurality of instructions;
• and
• one or more processors configured to access the memory, wherein the one or more processors are further configured to execute the plurality of instructions to:
• pre-aggregate training data for a plurality of employment positions associated with a plurality of employment position sources, wherein the training data is in one or more sequence file formats;
• convert the pre-aggregated training data into a set of sparse vectors;
• train a classifier model with the set of sparse vectors, wherein the classifier model is configured to determine a employment position source effectiveness for an employment position source, wherein the employment position source effectiveness is associated with a likelihood a job seeker will be hired for an employment position if the job seeker applies through the employment position source;
• receive candidate data relating to an employment position candidate;
• match the employment position candidate with one or more employment positions based on the received candidate data and the one or more employment positions;
• determine a suggested employment position for the employment position candidate from the one or more employment positions;
• and
• generate, for the suggested employment position, a predicted employment position source effectiveness associated with an employment position source of the plurality of employment position sources, the predicted employment position source effectiveness determined by applying the classifier model to the suggested employment position and to the received candidate data relating to the employment position candidate.

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising:

• instructions that cause the one or more processors to pre-aggregate training data for a plurality of employment positions associated with a plurality of employment position sources, wherein the training data is in one or more sequence file formats;
• instructions that cause the one or more processors to convert the pre-aggregated training data into a set of sparse vectors;
• instructions that cause the one or more processors to train a classifier model with the set of sparse vectors, wherein the classifier model is configured to determine a employment position source effectiveness for an employment position source, wherein the employment position source effectiveness is associated with a likelihood a job seeker will be hired for an employment position if the job seeker applies through the employment position source;
• instructions that cause the one or more processors to receive candidate data relating to an employment position candidate;
• instructions that cause the one or more processors to match the employment position candidate with one or more employment positions based on the received candidate data and the one or more employment positions;
• instructions that cause the one or more processors to determine a suggested employment position for the employment position candidate from the one or more employment positions;
• and
• instructions that cause the one or more processors to generate, for the suggested employment position, a predicted employment position source effectiveness associated with an employment position source of the plurality of employment position sources, the predicted employment position source effectiveness determined by applying the classifier model to the suggested employment position and to the received candidate data relating to the employment position candidate.