Diversity Measures and Equal Opportunity Practices in the North American Corporate Labour Force and Global Diversity Management Practices

Visier Solutions Inc hopes to create a new standard for the measurement of employee diversity in organizations based on scientific research and measured baselines. With the help of the interns, this study will set a standard for the practical measurement of diversity in organizations as well as best practices in the analysis of metric results. The findings of this study will enrich our understanding of diversity and equal opportunity treatment of diverse groups in the corporate environment.

Temporally consistent employee group labels

Analytical applications in large organizations across even intermediate time ranges are often made complex, costly or even impractical due to temporal inconsistencies in the available data. The ever-changing nature of organizations causes categorical labels in data to change over time. This is particularly true for HR data, as the organization adjusts to changes in skillsets, market and operations. This project aims at establishing automated methods of defining consistent employee group labelling across time.

Better predictions of employee events II

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.

Better predictions of employee events

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.