Data Scientist Project Intern (Trust and Safety-Monetization Integrity-Data Acquisition) - 2025 Start (BS/MS）

About the Team
The success of TikTok's data business model hinges on the supply of a large volume of high quality labeled data that will grow exponentially as our business scales up. However, the current cost of data labeling is excessively high. The Data Solutions Team uses quantitative and qualitative data to guide and uncover insights, turning our findings into real products to power exponential growth. The Data Solutions Team responsibility includes infrastructure construction, recognition capabilities management, global labeling delivery management.

We are looking for a motivated Data Science Intern to join our Data Acquisition function. As an intern, you will take ownership of a high-impact, scoped project designed to improve operational efficiency in TikTok's data labeling workflows. This internship offers exposure to real-world applications of data science in large-scale operational systems and a chance to work with a cross-functional team of data scientists, engineers, and business stakeholders.

Applications will be reviewed on a rolling basis - we encourage you to apply early.
Successful candidates must be able to commit to at least 3 months long internship period.
Successful candidates must be able to commit to the following internship cycles below:
- Off Cycle / Credit Bearing Internship - Starting from Aug 2025 (latest)
We will prioritize candidates who are able to commit to the above internship period. Please state your availability clearly in your resume (Start date, End date).

Responsibilities
1. Algorithm Development & Optimization: Design and implement matching algorithms and probabilistic models for task allocation and confidence scoring, focusing on multi-objective optimization that balances quality, cost, and efficiency constraints.
2. Feature Engineering & Data Pipeline Development: Extract meaningful signals from labeler performance data, task characteristics, and system logs to build robust features that power allocation decisions and confidence assessments.
3. Experimentation & Model Validation: Design and execute A/B tests, simulations, and validation experiments to measure algorithm performance, iterate on model improvements, and ensure real-world effectiveness.
4. Data Analysis & Insights Generation: Conduct exploratory analysis on platform data to identify patterns, build predictive models for operational forecasting, and generate actionable insights for product and business decisions.
5. Cross-functional Collaboration: Present findings and recommendations to engineering, product, and business teams, contributing to implementation planning and strategic decision-making processes.