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Trust and Security

Paolo A. Palma

Paolo A. Palma, a psychology doctoral student took a break from his studies to systematically review the definitions underpinning analytics on diversity. 

After a short visit to Vancouver from the University of Western Ontario, he documented how different companies define diversity across the globe, and the practices they employed to increase diversity and inclusion.

 “I already knew there wasn’t a single definition for diversity,” says Paolo, “but I never expected such a large variation [in data collection and reporting standards]. There are also a lot of diversity practices that aren’t very well validated.”  

We agree and hope others can help us move these research findings into industry.

Paolo’s journey with inclusion has also helped guide his research topics. He was an operations administrator at a large financial institution. The “company itself was diverse, people were still segregated [in their interactions] and who their friends were. So obviously having diversity in terms of numbers isn’t all there is, so I wanted to look at inclusion.”

Paolo feels meaningful inclusion is when people value you and your differences for what they are, rather than valuing them based on what they can contribute. 

He highly recommends social scientist work at Visier. “A lot of social scientists want to have an impact in the real world, but don’t have a lot of opportunities for real-world experience. I think Visier is a great place to work.”

Nafiseh Sedaghat

Nafiseh Sedaghat, a doctoral student in computer science and bioinformatics, joined Visier for a data science cooperative education student. 

She looked at our data model to see what additional features could be extracted from the existing attributes of employees. As well, Nafiseh evaluated different techniques to cross-validate our predictive models. The results of her work informed how we provide predictions in our Visier People® solution.

Instead of predicting drug resistance in pathogenic bacteria, Nafiseh looked at time-series classification to improve the performance of predictive models. She explored other techniques and since the dataset she was exploring was large, she learned and used Scala, a programming language. Her work informed our use of machine learning to predict employee events around retention, promotion, and movement.

“My experience at Visier was fantastic,” says Nafiseh. “I felt like I was a member of a great team and had a nice manager.”  

We are grateful for financial and academic support from Mitacs, a not-for-profit organization that connects researchers with industry, and Dr. Leonid Chindelevitch, then of Simon Fraser University and now with Imperial College London. Most importantly we thank Nafiseh Sedaghat for joining us and giving valuable feedback on the program. 

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