Determinants of Offered Wage Level in Russia’s IT Sector: Internet Recruitment Data and Ensemble Models

This article analyses the determinants of offered wage level in Russia’s IT sector using internet recruitment data. The aim of the study is to assess the predictive power of the structural characteristics of job postings and to rank their contribution using non-linear ensemble machine learning methods. The empirical basis is a dataset of job postings for 2025 retrieved through the open service HeadHunter API. This source compares favorably with standard sources of job vacancy data in its higher update frequency and more detailed occupational segmentation. The infiuence of five groups of factors is examined: work experience, professional role, regional group, work schedule and type of employment. The study tests hypotheses on the dominant role of experience, the advantage of a non-linear ensemble of models over linear regression, and the weakening of regional differentiation factor under the infiuence of remote employment. A weighted ensemble of models (Extra Trees, Random Forest, Gradient Boosting) is proposed, in which the weights of the components are proportional to their predictive accuracy. Linear regression is rejected by diagnostic tests and is used as a reference. The results show that non-linear models outperform linear regression in terms of the coefficient of determination. Work experience is found to be the key factor determining the wage level, while the infiuence of work schedule and type of employment is minimal. Remote work only partially smooths regional wage differences. The study confirms that internet recruitment data are an effective instrument for the timely monitoring of the labour market, complementing official statistics. A calibration adjustment is proposed for harmonising these sources.