Why Do Data Scientists Want to Change Jobs: Using Machine Learning Techniques to Analyze Employees' Intentions in Switching Jobs release_utz6axzbijfsbmsiygs4knkjwa

by Sumali J. Conlon

Published in INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY by CIRWOLRD.

2021   Volume 16, p59-71

Abstract

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%.
In application/xml+jats format

Archived Files and Locations

application/pdf  507.6 kB
file_c2drrlh5vzf7bde6bzgsfpc7mu
rajpub.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-06-07
Journal Metadata
Not in DOAJ
Not in Keepers Registry
ISSN-L:  2278-5612
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 374a8b46-04b6-49be-9540-bacfa83c94a6
API URL: JSON