Sungjoon Yoon

Co-founder, CTO at Metric Studio Inc. (NNT)

Sungjoon Yoon has a diverse work experience in various roles and industries. Sungjoon co-founded Metric Studio Inc. (NNT) in 2020, where they serve as the Co-founder and CTO. Prior to this, Sungjoon worked as a Software Engineer for GameRecipe from 2019 to 2020. Sungjoon also gained experience as a Quant at Hanwha Investment & Securities from 2018 to 2019. Sungjoon co-founded and served as the CTO of Desire Lab from 2016 to 2018, where they worked on the YOIL project. Earlier in their career, they worked as a Software Engineer at Kakao Corp from 2014 to 2016, a Quant at NH투자증권 from 2013 to 2014, and as a CTO at IUM SOCIUS, Inc. from 2011 to 2013. Sungjoon also worked as a Software Engineer at NAVER Corp from 2009 to 2011 and participated in a Winter Internship Program at NAVER Corp in 2007. Sungjoon began their career as a Research Assistant at Arthur D. Little in 2008.

Sungjoon Yoon received their Bachelor of Science degree in Computer Science and Business Economics Program from the Korea Advanced Institute of Science and Technology (KAIST) from 2002 to 2009. Prior to that, they attended Hansung Science High School from 2000 to 2002. Sungjoon also obtained various certifications, including a Digital Strategist certification from Braze in 2023, a Google SEO Fundamentals certification from Coursera Course Certificates in 2023, and multiple certifications from Coursera Course Certificates in areas such as Blockchain, Machine Learning, and Data Science, obtained between 2014 and 2021.

Location

Seongnam, Republic of Korea

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Metric Studio Inc. (NNT)

데이터 분석과 마케팅 자동화 컨설팅 서비스를 제공합니다. • 데이터 분석 데이터 수집, 유저 리텐션, 지표 개선에 필요한 데이터 분석 서비스를 제공합니다. • CRM과 마케팅 자동화 유저 온보딩과 유저 리텐션 증대를 위한 다양한 마케팅 자동화 컨설팅 및 운영 서비스를 제공합니다. • 인앱메시지 (In-App Message) 앱에서 사용되는 인앱메시지의 코드와 디자인을 제공합니다. Braze, Firebase 등 다양한 솔루션의 인앱메시지의 코드를 제공하며 HTML/CSS/JavaScript와 코틀린/스위프트를 통해 인앱메시지를 구현해드립니다. • 예측모형 (Predictive Modeling) 유저 행동 분석 툴 (ex. Amplitude, Firebase, Mixpanel), CRM 툴 (ex. Braze), Attribution 툴 (ex. Appsflyer)의 데이터를 통해 예측 모형을 만듭니다. 예측 모형을 통해서 유저 구매와 LTV를 예측합니다.


Employees

11-50

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