Data Scientist, Product Infrastructure (Customer Service) - TikTok

TikTok-Data Science team is responsible for all data science and analytics related work, cooperating with all key value chains in TikTok, including 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 scenarios.



What You'II Do

- Work with large and complex data to solve technical and statistical challenges, and drive business impact.

- Gain an in-depth understanding of the business logic and user demand scenarios in the customer service domain, assess the rationality and accuracy of the metric system based on business goals, and efficiently monitor and provide early warnings on business performance.

- Collaborate with recommendation algorithms, product, and operations teams, analyze data to clarify current business issues based on project objectives, identify potential optimization directions for user experience, and provide effective data insights and momentum for overall business development.

- Review and analyze the current issues in the customer service data pipeline, collaborate with business teams to push forward improvement plans, correct and optimize problems, and provide accurate data-driven decision support for business iteration.

- Explore and summarize analytical methodologies, study analysis theories, and drive the iterative optimization of data products to improve analytical efficiency.