Workforce Analytics: A Data-Driven Machine Learning Approach to Predict Job Change of Data Scientists

Authors

  • Sohini Sengupta Welingkar Institute of Management, Mumbai
  • Sareeta Mahendra Mugde
  • Renuka Deshpande
  • Kimaya Potdar

DOI:

https://doi.org/10.33182/tmj.v9i2.1574

Keywords:

Data Science, Big Data, Workforce Analytics, HR Analytics, Attrition, Business Strategy

Abstract

Today the total amount of data created, captured, and consumed in the world is increasing at a rapid rate, as digitally driven organizations continue to contribute to the ever- growing global data sphere. (Holst, Statista Report 2020). This data brings with it a plethora of opportunities for organizations across different sectors. Hence, their hiring outlook is shifting towards candidates who possess the abilities to decode data and generate actionable insights to gain a competitive advantage. A career in data science offers great scope and the demand for such candidates is expected to rise steeply. When companies hire for big data and data science roles, they often provide training. From an HR perspective, it is important to understand how many of them would work for the company in the future or how many look at the training with an upskilling perspective for new jobs. HR has the aim of reducing costs and time required to conduct trainings by designing courses aligning to the candidate’s interest and needs. In this paper, we explored the data based on features including demographics, education and prior experience of the candidates. We made use of machine learning algorithms, viz. Logistic Regression, Naive Bayes, K Nearest-Neighbours Classifier, Decision Trees, Random Forest, Support Vector Machine, Gradient Descent Boosting, and XGBoost to predict whether a candidate will look for a new job or will stay and work for the company. 

Published

2021-08-31

How to Cite

Sengupta, S., Mugde, S., Deshpande, R. and Potdar, K. (2021) “Workforce Analytics: A Data-Driven Machine Learning Approach to Predict Job Change of Data Scientists”, Transnational Marketing Journal. London, UK, 9(2), pp. 335–349. doi: 10.33182/tmj.v9i2.1574.