(untitled) (INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY, 2021)
Data scientists are among the highest-paid and most in-demand employees in the 21st century. This gives them opportunities to switch jobs quite easily. In this paper, we follow the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and the data science life cycle process to analyze factors which predict whether a data scientist is looking for a new job or not. Specifically, we use machine learning techniques to analyze data from Kaggle.com. We find that features that have the highest impact on whether a data scientist wants to change his/her job include the city development index, company size, and company type. When we examine the city development index more carefully, we find evidence suggesting that employees move from cities with lower to higher development indexes, as they become more experienced. The predictive analysis system we use is able to predict with average accuracy rates of higher than 78%.