Job Description

Take charge of data analytics for payment and financial segments of global payment business, and conduct in-depth analysis on transactions and financial metrics for TikTok’s core businesses including e-commerce, live streaming and advertising.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.

Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.

Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.

Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

- Opportunity Identification: Gain in-depth insights into user behaviors, deliver optimization recommendations via qualitative and quantitative analysis to identify new growth drivers for the business.
- Metric System Development: Design core metric frameworks aligned with the business value chain, align team objectives and unified measurement standards, and guide the iteration of models and operational strategies.
- Business Improvement Enablement: Evaluate operational strategies with experimental analysis, causal inference and statistical modeling, drive strategy optimization to boost business revenue and decision-making efficiency.
- Agentic Recommendation System: Explore the application of LLMs in data analytics, covering user behavior analysis, crowd segmentation, customer acquisition analysis, growth analytics and profitability analysis, and advance the implementation of the new paradigm of Agentic Data Analysis.
- AI for Data Science: Build AI-powered capabilities for data science teams. Leverage LLMs and Agents to construct automated tools for anomaly attribution, experiment analysis and insight generation; accumulate reusable data science toolkits and standardized analysis frameworks, and build an AI-driven data science team.