Price: 0
Number of applications: 3
03.04.25 (inclusive)
Interaction and recruitment for work under the contract
Finished product
ICT tasks
Robotics
Neurotechnology and artificial Intelligence
Purpose and description of task (project)
A model for classifying professional categories based on a text job description using NLP and machine learning. The goal is to automate the analysis of vacancies, accelerate the selection of candidates and reduce manual labor.
Software/ IS
Currently, job analysis and classification require significant time and effort, as text descriptions are processed manually. This leads to inaccuracies in defining professional categories, slowing down the recruitment process, and increasing the burden on recruiters. The lack of automated analysis complicates the systematization of data, which reduces the effectiveness of employment platforms and recruitment agencies. To solve this problem, a machine learning model is needed that uses natural language processing (NLP) techniques to automatically classify vacancies. This solution will speed up the analysis process, minimize the impact of the human factor, and improve the accuracy of candidates' compliance with employers' requirements.
Reduction of time for job processing and classification ~ 90% Reduction of labor costs ~ 80% (requires only monitoring and model adjustment) Reduction of financial costs ~ 80% Error reduction (human factor) ~ 80%
Digital Government Support Center
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