TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.
About the Team
TikTok Live Data Science team is responsible for Live growth, ecosystem and revenue strategy analytical work. We work closely with Algorithm team and Product team. The goal of the team is to generate actionable insights from data, driving Live to create an environment that brings communities together in real time to create meaningful and interactive connections around the globe. Our main tasks include metrics defining, root cause analysis, experimentation methodology, strategy evaluation and exploratory analysis to find more opportunities.
About the Role
The primary role of a Data Scientist is to conduct deep analysis of user behavior and content ecosystems to generate business insights that could be applied to actionable improving initiatives. You will work closely with cross-function teams, such as PM\RD\MLE, to improve user experience and fulfill the growth of TikTok Live
Responsibilities - What You'II Do
1. Conduct data analysis in LIVE related business, including watching experience, creator ecosystem, agency management, algorithm improvement and etc..
2. Design metrics framework to measure product healthiness, keep tracking of core metrics and understand root causes of metric movements.
3. Conduct scientific evaluation with statistical methods, including A/B testing and casual inference.
4. Identify growth opportunities with data analytics, and drive business decisions. Work with PM/MLE/RD to deliver product and strategy improvement.
5. Research data science theories and methodology, improve analysis efficiency and data product tools.
