Data Scientist Intern (TikTok-Growth Analytics-Data Science)- 2026 Start (BS/MS)

Responsibilities

About the team
The User Growth team (UG) at TikTok plays a core role in the acquisition, activation, and retention of billions of users through our globally popular products such as TikTok, Lemon8, CapCut, etc. We are building platforms, leveraging data & ML models, and providing end-to-end solutions to power the global growth of TikTok products. The creative team at UG is looking for a talented and self-directed senior professional to generate, manage, and evolve the creative initiatives for our suite of industry-leading products.

We are looking for talented individuals to join us for an internship in 2026*. Internships at TikTok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok.

Successful candidates must be able to commit to either of the following Internships
- From January to June I
- From May to August (Summer)

Responsibilities:
- Responsible for data analysis of international short video products, analyzing key business indicators such as user growth and retention, and providing data support and decision-making suggestions;
- Build a growth indicator system and target management mechanism, construct and iterate the data visualization system, and provide data insights and analysis support for business growth;
- Work closely with product, strategy, marketing, and R&D departments to promote the implementation of conclusions and bring business-wide improvements;
- Design analysis plans such as A/B experiments and causal inference models, verify or deduce business optimization and decision-making conclusions, and provide efficient analysis methods;
- Responsible for decomposing and diagnosing the driving factors of business growth, investigating and attributing data anomalies, and building automated anomaly attribution tools;
- In supporting business development, be good at thinking and summarizing, and precipitate growth analysis methodology and data efficiency tools.