A computer-implemented method for searching users that meet one or more labor market search requirements, comprising: obtaining configuration profiles of computing systems operated by sample users, wherein the users have at least one determined characteristic and wherein the at least one characteristic indicates at least user's technical experience; generating a machine learning model that associates the determined characteristic of the sample users with the configuration profiles of the computing systems of the sample users, wherein the machine learning model is configured to correlate the characteristic indicating a specified time duration of technical experience with a programming language with the configuration profiles indicating files are stored on a system of the target user that have a file extension indicating source code files of the programming language and that have a file modification date greater than or equal to the specified time duration; receiving a search query comprising one or more search requirements for target users that match the at least one determined characteristic, wherein the one or more search requirements include at least the specified time duration of technical experience; accessing, by a processor, configuration profiles of computing systems of a plurality of target users, wherein said configuration profiles include at least information about hardware, software and data files of computing systems of said target users indicating technical experience of respective users; analyzing, by the processor, the configuration profiles of the computing systems of said target users using the machine learning model to identify one or more target users that match the one or more search requirements of the search query, wherein analyzing the configuration profiles comprises analyzing one or more software and data files of computing systems of said target users; and identifying, by the processor, at least one target user that matches the at least one determined characteristic specified in the search query based on analysis of the configuration profile of the computing system of said target user by the machine learning model, wherein the machine learning model is modified by a training process comprising: obtaining the configuration profiles of the computing systems of the sample users having the at least one determined characteristic; executing the machine learning model using at least one characteristic of the sample user as an input value; and correcting the machine learning model by performing back propagation based on a comparison of output values of the machine learning model to the configuration profiles, wherein the output values indicate association of the characteristics of the sample users with the information about hardware, software and data files of computing systems of the sample users. obtaining configuration profiles of computing systems operated by sample users, wherein the users have at least one determined characteristic and wherein the at least one characteristic indicates at least user's technical experience; generating a machine learning model that associates the determined characteristic of the sample users with the configuration profiles of the computing systems of the sample users, wherein the machine learning model is configured to correlate the characteristic indicating a specified time duration of technical experience with a programming language with the configuration profiles indicating files are stored on a system of the target user that have a file extension indicating source code files of the programming language and that have a file modification date greater than or equal to the specified time duration; receiving a search query comprising one or more search requirements for target users that match the at least one determined characteristic, wherein the one or more search requirements include at least the specified time duration of technical experience; accessing, by a processor, configuration profiles of computing systems of a plurality of target users, wherein said configuration profiles include at least information about hardware, software and data files of computing systems of said target users indicating technical experience of respective users; analyzing, by the processor, the configuration profiles of the computing systems of said target users using the machine learning model to identify one or more target users that match the one or more search requirements of the search query, wherein analyzing the configuration profiles comprises analyzing one or more software and data files of computing systems of said target users; and identifying, by the processor, at least one target user that matches the at least one determined characteristic specified in the search query based on analysis of the configuration profile of the computing system of said target user by the machine learning model, obtaining the configuration profiles of the computing systems of the sample users having the at least one determined characteristic; executing the machine learning model using at least one characteristic of the sample user as an input value; and correcting the machine learning model by performing back propagation based on a comparison of output values of the machine learning model to the configuration profiles, wherein the output values indicate association of the characteristics of the sample users with the information about hardware, software and data files of computing systems of the sample users.