Kirill Gugaev
About Kirill Gugaev
Kirill Gugaev is the Chief Technology Officer at Corrily, based in the San Francisco Bay Area. He has extensive experience in financial machine learning, trading, and cryptocurrency, having previously held positions at Startrek Crypto, Yandex LLC, and OLX Group.
Current Role at Corrily
Kirill Gugaev serves as the Chief Technology Officer at Corrily, a position he has held since 2022. Based in the San Francisco Bay Area, he plays a significant role in shaping the company's technology strategy and overseeing the development of innovative solutions. His leadership is pivotal in driving the company's efforts in the financial technology sector.
Previous Experience in Technology and Data Science
Before joining Corrily, Kirill Gugaev held various roles in technology and data science. He was the Founder of Startrek Crypto from 2022 to 2023. He also worked at Yandex LLC as an Antifraud & Antispam Data Scientist from 2018 to 2019. His experience includes developing algorithms that support revenue growth for clients with significant annual recurring revenue.
Educational Background in Mathematics and Data Science
Kirill Gugaev obtained a Master's degree in Applied Mathematics and Mechanics from Lomonosov Moscow State University, where he studied from 2010 to 2015. He further enhanced his expertise by completing a rigorous two-year program in Math and Data Science at the Yandex School of Data Analysis from 2018 to 2019.
Expertise in Financial Technology and Machine Learning
Kirill Gugaev possesses expertise in financial machine learning, trading, and cryptocurrency. His knowledge aligns with his passion for the FinTech industry, where he has contributed to various projects and initiatives. His work has focused on leveraging data science to drive innovation and efficiency in financial services.
Contributions to the Tech Community
In addition to his professional roles, Kirill Gugaev has contributed to the tech community through thought leadership. He authored a post for Forbes Council discussing the advantages of Bayesian-style experimentation, such as multi-armed bandit approaches, compared to traditional A/B testing methods.