Talent science / analytics

Talent Analytics has emerged as a key area of competitive advantage in the modern economy. The progress made in Natural Language Processing, particularly with Deep Learning models since 2012, has been dramatic.

We have developed dozens of algorithms and approaches in the field of talent.

Please see custom solutions and case studies for more.


our CORE algorithm encodes the language of recruitment

A simplified model of high dimensional spaces generated through deep learning models applied to millions of data points, that encode the language of recruitment (not a terrible strain of bacteria)

A simplified model of high dimensional spaces generated through deep learning models applied to millions of data points, that encode the language of recruitment (not a terrible strain of bacteria)


Our algorithm enables you to create high quality, nuanced “job to candidate matching” at scale.


untapt’s proprietary core model uses Neuro-linguistic programming and involves 14m artificial neurons arranged within a 16 layer Deep Neural Network. As suggested by the diagram these layers are spread across three functional areas:

  1. Word Level: CVs and JDs are input into the model; the meaning of individual words from these documents are encoded.

  2. Candidate & Job Level: Words are considered holistically, in context, to “understand” both candidate and job opening requirements

  3. Match Level: The model combines the candidate and job encodings together to output a prediction of how well the CV fits the JD.

 


All models are wrong, but some are useful
— George Box