Woodz Kim is a Staff Data Scientist and technological leader with 11 years of hands-on experience and 14 years of leadership in the mobile and data industries, currently leading Moloco’s streaming monetization data science team from Seoul. He blends deep technical fluency across cloud data engineering (Azure, AWS, GCP), big data stacks (Hadoop), multiple languages (Java, Kotlin, Scala, Python, Swift, JavaScript) and modern AI (deep learning, LLMs) with a proven track record of business impact—doubling SOCAR’s revenue over five years and driving per-vehicle revenue up 10% annually. As Head of Data at VCNC he helped scale the "Between" app to 30 million downloads and sustained high user satisfaction, and as an Apache Zeppelin committer he contributed UI and backend improvements to a widely used open-source notebook. Known for building and mentoring large teams (recruiting and training 150+ data professionals) and spinning internal products into new revenue streams, he combines entrepreneurial instincts with production-grade engineering and cross-functional execution. An unusual strength is his ability to translate cutting-edge AI capabilities into operational products (e.g., AI Contact Center with Azure OpenAI) while maintaining rigorous product metrics and customer focus.
11 years of coding experience
8 years of employment as a software developer
Master of Science - MS, Electrical Engineering and Computer Science, Master of Science - MS, Electrical Engineering and Computer Science at Seoul National University
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
Role in this project:
Full-stack Developer
Contributions:1 commit, 3 PRs, 21 comments in 1 day
Contributions summary:Woodz's contributions primarily focused on enhancing the Apache Zeppelin web interface and fixing backend issues related to notebook management. They implemented features such as confirming note deletions and inserting new paragraphs. Additionally, the user addressed bugs in the Spark SQL interpreter and improved the user interface by adding features like view-only mode and chart improvements. Furthermore, they refactored code for cleaner and more efficient notebook updates.
Open-source keyboard firmware for Atmel AVR and Arm USB families
Contributions:2 PRs, 18 pushes in 7 months
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