Elyse Go
About Elyse Go
Elyse Go is a Data Science Manager specializing in Recommendations and Ranking at Kumu, where she has worked since 2022. She has a background in data science and finance, with previous roles at Cobena Business Analytics, Kumu, ZeroSix AI, PayMaya, and Grab.
Current Role at Kumu
Elyse Go serves as the Data Science Manager for Recommendations and Ranking at Kumu. She has held this position since 2022, focusing on enhancing user engagement through data-driven strategies. In her current role, she manages a team dedicated to improving new user activity and retention by utilizing advanced statistical and machine learning models.
Previous Experience at Kumu
Prior to her current role, Elyse worked at Kumu as a Senior Data Scientist in the Recommendations and Ranking department from 2021 to 2022. During this time, she oversaw 30 A/B test experiments aimed at increasing average user watch duration and activation rates for treatment groups. Her contributions were significant in optimizing user experience on the platform.
Professional Background
Elyse has a diverse professional background, having worked in various roles related to data science and analytics. She was a Business Analyst at Cobena Business Analytics & Strategy, Inc. from 2019 to 2020. She also held the position of Data Science Lead at ZeroSix AI + Data Science Consulting from 2020 to 2021, and worked as a Strategy and Programs Associate at PayMaya Philippines from 2018 to 2019. Additionally, she was a Retention and Optimization Associate at Grab from 2016 to 2018.
Education and Training
Elyse Go studied at De La Salle University, where she earned a degree in Finance and Financial Management Services, focusing on the Management of Financial Institutions from 2012 to 2016. She further enhanced her skills in data science by completing a Data Science Scholar program at FTW Foundation in 2019, which lasted for 11 months.
Technical Leadership and Initiatives
In her roles, Elyse has led engineering initiatives that include developing a unit testing pipeline and a platform scalability service. She has also implemented holdout testing to streamline the experimentation pipeline, which has improved collaboration and efficiency across teams. Her leadership in these projects reflects her commitment to advancing data science practices within her organizations.