AI & Machine Learning


Large international IT company, 1200+ employess in 3 countries, 100+ clients.

To reduce the turnover of front office employees (developers, testers, system analysts), we need to identify a set of key factors affecting a significant increase in the probability of employee termination.

Project Results
A developed model using AutoML approaches (the best result was obtained using the Distributed Random Forest algorithm), AUC = 0.64 and recall of terminated employees is approximately 0.68. This model helps identify employees who are in the "risk zone" based on more than 50 factors which, in turn, enables vertical managers and HRs to specifically intensify actions to retain employees and take timely measures.

Business Value
Developed model has been integrated into the HR monitoring dashboard of the Client which helped to improve staff care and reduce churn of highly qualified IT personnel for more than 5% annually.

Tech Stack
Python, Airflow, Grafana, PostgreSQL.