Data Scientist, Research - TikTok

About TikTok
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 products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. To us, every challenge, no matter how ambiguous, 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 users we serve. Join us.

TikTok-Data Science team is responsible for all data science and analytics related work, cooperating with all key value chains in TikTok, including Privacy, User Growth, PGC, Content Ecosystem, Social, Creation, Product Infrastructure, Research and Science etc. The goal of the team is to generate actionable insights from data and help the stakeholders to make the right decisions. Our main tasks include metrics defining, root cause analysis, experimentation methodology, feature/strategy evaluation and exploratory analysis to find more opportunities.

The primary role of a Data Scientist for our Data team is to conduct deep analysis into user behavior, product features and content ecosystem to generate business insights that could be applied to actionable improving initiatives. You will work closely with cross-function teams, such as Product Management, Research and Development, Machine Learning Engineering, to improve user experience and fulfill growth of TikTok in all different regions.

Responsibilities - What You'II Do
- Responsible for the design of TikTok's online decision-making mechanism, optimization of experimental frameworks and quantitative evaluation methods, user and content understanding and mining, and other general issues to improve decision-making and analysis efficiency
- In-depth understanding of the difficulties of product and recommendation in experimental evaluation and online decision-making, design reasonable solutions, and promote implementation
- Make advanced statistical or machine learning processing for difficult A/B experiments (weak significance, weak observation, bilateral effects, network effects, etc.), and provide effective analytical conclusions
- Provide effective solutions for deep understanding of users and content through machine learning, statistical mining, LLM, and other technologies