Vasya (Roger) Selsov
About Vasya (Roger) Selsov
Vasya (Roger) Selsov serves as the Principal Data Scientist at Zeenk since 2021 and has a strong academic background in Statistics and Physics & Mathematics from Texas A&M University and the University of Massachusetts Amherst.
Work at Zeenk
Vasya Selsov has been serving as Principal Data Scientist at Zeenk since 2021. In this role, he focuses on enhancing data-driven decision-making processes by engineering key features from large and complex datasets. His work includes designing controlled experiments to evaluate outreach campaigns, which has led to improved member engagement through strategic changes in language elements.
Current Role at Harvard University
In addition to his position at Zeenk, Vasya Selsov has been a Teaching Assistant at Harvard University since 2021. His responsibilities include supporting the educational process and assisting students in understanding complex data science concepts. His dual roles in academia and industry reflect his commitment to both teaching and practical application of data science.
Education and Expertise
Vasya Selsov earned a Master's degree in Statistics from Texas A&M University, where he studied from 2012 to 2016. Prior to that, he completed a Bachelor of Science in Physics and Mathematics at the University of Massachusetts Amherst from 2003 to 2008. His educational background provides a strong foundation for his expertise in data science and statistical analysis.
Previous Experience
Before joining Zeenk, Vasya Selsov worked as a Senior Data Scientist at Nanigans from 2018 to 2021, where he implemented machine learning techniques that enhanced model performance. He also served as a Senior Systems Analyst at Massachusetts General Hospital from 2010 to 2016, gaining valuable experience in data analysis and system optimization.
Achievements in Data Science
Vasya Selsov has made significant contributions to data science through his work on member segmentation and outreach strategies. He refined member segmentation by identifying key attributes influencing behavior and engagement, which has led to more targeted outreach efforts. His implementation of machine learning techniques improved the area under the receiver operating characteristic (AUROC) curve by 15% compared to existing models.