Our talent matching model can recommend a shortlist of candidates for each open role, allowing hiring managers to make faster, data-informed decisions.
Our model does this by taking in information about each candidate to infer their skills, then doing the same for each role to understand the required skills. Using this information, it looks at the degree of similarity between the two skillsets to predict the likelihood of the candidate being successful in a role.
For more information on how our modeling works, head to the Modeling Approach section.
Head to either the Preview Candidates Score section (if you are looking at a single open role) or the Preview Applications Score section (if you are looking across multiple open roles) of our API Reference Documents.
|For each candidate:|
- A resume or their job titles, employers, skills, education
- Assessment results, interests, and additional information*
|Match quality ranking (low to very high) and match score for each candidate|
|For each role:|
- Job title, employer, skills, education
- Job description*
|List of the top skills overlaps and gaps for each candidate|
The inputs and outputs with an * (as well as any other data specific to your organization) can be incorporated through a customized model. To learn more, please reach out.
Within an ATS or other talent matching system, we can score candidates who have applied for a role, enabling hiring managers to more efficiently sort candidates and identify candidates who may otherwise be overlooked.
Updated about 2 months ago