MULTIFACTOR MODEL FOR ASSESSING AND FORECASTING SALARIES IN THE IT OUTSTAFFING SECTOR OF UKRAINE

This article presents the development and application of a multifactor model for forecasting the salaries of IT professionals employed under the outstaffing model in Ukraine. The model is based on empirical data collected over a ten-year period (2014–2023) and takes into account a wide range of factors that significantly influence wage levels. These include years of experience, job title (e.g., Software Engineer, QA, DevOps), domain specialization (e.g., Fintech, Security, Automotive), level of English proficiency, company size, education, and geographic location. The forecasting algorithm is implemented using a Random Forest Regressor, which provides both high accuracy and transparency in terms of feature importance. A key feature of the study is its emphasis on the managerial interpretation of salary dynamics. Rising salaries in specific domains are treated not only as compensation-related phenomena, but also as indicators of domain-level growth and increased client-side demand. In particular, such domains as Security, Automotive, Edtech, and Telecom demonstrated strong salary growth trajectories. For each domain, predictions were generated for the years 2024 to 2026, followed by validation of the 2024 results using actual collected data. This cross-check confirmed the predictive value of the model and its usefulness for strategic decisionmaking. The study contributes to the field of management by offering a practical analytics tool for decision-makers in IT companies, especially those operating under the outstaffing model. It enables executives, HR managers, and financial planners to anticipate compensation trends, identify growing domains, and align internal talent strategies with external market dynamics. The proposed model can also support long-term planning related to service portfolio diversification and resource allocation. Overall, the research provides both theoretical insight and practical utility, enhancing data-driven management in the highly dynamic IT labor market of Ukraine.