Why Do Data Scientists Want to Change Jobs: Using Machine Learning Techniques to Analyze Employees’ Intentions in Switching Jobs
Keywords:Machine Learning, Data Scientists, Employee Turnover, Predictive Analytics
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%.
Alao, D. & Adeyemo, A. (2013). Analyzing employee attrition using decision tree algorithms. Computing, Information Systems & Development Informatics Vol. 4 No. 1 March, 2013. 17–28.
Alduayj S. S. & Rajpoot K. (2018) "Predicting Employee Attrition using Machine Learning," 2018 International Conference on Innovations in Information Technology (IIT), pp. 93-98, doi:10.1109/INNOVATIONS.2018.8605976
Burtch Works, 2019. “10 Reasons for Data Scientists and Analytics Pros to Consider a Job Change.” Retrieved from https://www.burtchworks.com/2019/04/15/10-reasons-for-data-scientists-and-analytics-pros-to-consider-a-job-change/
Burtch, L. (2020). “The Burtch Works Study Salaries of Data Scientists & Predictive Analytics Professionals.” Retrieved from https://www.burtchworks.com/wp-content/uploads/2020/08/Burtch-Works-Study_DS-PAP-2020.pdf.
Davenport, T. H. (2012). “Data Scientist: The Sexiest Job of the 21st Century.” Harvard Business Review 90(10):70-6, 128.
DataRobot. Retrieved from https://www.datarobot.com/
Dutta, S. (2019). “What Skills Do You Need to Become a Data Scientist?” Retrieved from What Skills Do You Need to Become a Data Scientist? (springboard.com).
Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). “Predicting Employee Attrition Using Machine Learning Techniques.” Computers 9(4), 86. https://doi.org/10.3390/computers9040086.
Golestani, A., Masli, M., Shami, N. S., Jones, J., Menon A., & Mondal J. (2018). Real-Time Prediction of Employee Engagement Using Social Media and Text Mining. 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018, 1383-1387.
Indeed.com. “Work Experience and Your Career: Definition, Importance and Tips.” Retrieved from https://www.indeed.com/career-advice/finding-a-job/work-experience
Kaggle.com dataset. “HR Analytics: Job Change of Data Scientists Predict who will move to a new job.” Retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/code.
Kooiman, N., Jan Latten J., & Bontje M. (2018). "Human Capital Migration: A Longitudinal Perspective." Journal of Economic and Human Geography. https://doi.org/10.1111/tesg.12324
Korpi, M., & Clark, W. (2017). Human Capital Theory and Internal Migration: Do Average Outcomes Distort Our View of Migrant Motives? Migration letters: an international journal of migration studies, Volume 14, Number 2, 237–250.
Locke, E.A. (1976). The nature and causes of job satisfaction. In M.D. Dunnette (Ed.), Handbook of industrial and organizational psychology, Chicago: Rand McNally. Vol. 1, 1297-1343.
Massey, D., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. (1993). Theories of International Migration: A Review and Appraisal. Population and Development Review, Volume 19, Number 3, 431-466. doi:10.2307/2938462.
Mobley, W. H. (1982). Employee turnover: Causes, consequences, and control, Reading, MA: Addison Wesley.
Morrell, K., Loan-Clarke, J., & Wilkinson, A. (2001). Unweaving leaving: The use of models in the management of employee turnover. International Journal of Management Reviews, Vol. 3, 219-44. https://doi.org/10.1111/1468-2370.00065.
Mossholder, K., Sutton, R. P., & Henagan, S. C. (2005). A relational perspective on turnover: Examining structural, attitudinal, and behavioral predictors. Academy of Management Journal, Vol. 48, No. 4, 607- 18. https://doi.org/10.2307/20159682.
Rethinking Data (2020). “Put More of Your Business Data to Work — From Edge to Cloud” Retrieved from Rethink_Data_Report_2020.pdf (seagate.com).
Otto N. (2017). “Avoidable turnover costing employers big.” Retrieved from Avoidable turnover costing employers big | Employee Benefit News.
Pattabiraman, K. (2019) “The Most Promising Jobs of 2019.” Retrieved from https://blog.linkedin.com/2019/january/10/linkedins-most-promising-jobs-of-2019.
Punnoose, R. & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms. International Journal of Advanced Research in Artificial Intelligence, Vol. 5, Issue 9. 22–26. https://doi.org/10.14569/IJARAI.2016.050904.
Shearer, C. (2000) “The CRISP-DM Model: The New Blueprint for Data Mining”, Journal of Data Warehousing, Volume 5, Number 4, page. 13-22, 2000.
Sjaastad L.A.(1962) The Costs and returns of human migration. Journal of Political Economy, Volume 70, Number 5, Part 2, 80–93. https://doi.org/10.1086/258726
Spector, P.E. (1997). Job satisfaction: Application, assessment, causes and consequences. Thousand Oaks, CA: SAGE.
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Copyright (c) 2021 Sumali J. Conlon
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