Webinar "Predicting Employee Burnout at Scale"

To access this webinar, please register here: https://hubs.li/Q01zkQQP0

Topic: "Predicting Employee Burnout at Scale"

Speaker#1: Brittany Schmaling, Sr. Data Analyst at Ceridian

A gifted storyteller, Brittany has a wealth of experience making sense of volumes of data to help business leaders solve real world people problems. She holds a Masters in Industrial Organizational Psychology and an ACC credential from the International Coaching Federation.

Speaker#2: Shan Krishnasamy Venugopal, Sr. Data Scientist at Ceridian

His primary responsibilities include designing scalable ML solutions using big data solutions and designing data privacy systems to enable data science without compromising the user privacy. He also holds a master’s degree in electrical and computer engineering with specialization in data science from the University of Ottawa.

Speaker#3: Seng Kin Chu, Data Scientist at Ceridian
Her unique experience allows her to efficiently solve problems for businesses at scale. Right now, her primary objective is to leverage the power of data science to help businesses around the globe through Dayforce. She also holds a master's degree in Analytics from Georgia Institute of Technology.

Abstract:
An organization’s most important asset is its people. Every organization collects and stores thousands of data points about their employees. With this comes ethical considerations of how to treat the data, what it can be used for, and how to effectively use the data to help the organization accomplish its goals. Burnout was a huge buzzword in the news in 2022 and will continue to remain top of mind for organizations in 2023.

Join us to learn about how we used deidentification and feature selection on employee data across different clients and industries to create models that accurately predict who will burnout.

Key takeaways for attendees:
- How to define and validate a people measure like burnout;
- The practices used to keep data secure when working with people data;
- How to leverage feature selection to identify additional features to measure burnout

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