Job Description

Team Introduction

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.

As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests.

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.

We are looking for a motivated Data Science Intern to join of our Data Solutions team. 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 crossfunctional team of data scientists, engineers, and business stakeholders.

• 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.
• 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.
• 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
• 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.
• Cross-functional Collaboration: Present findings and recommendations to engineering, product, and business teams, contributing to implementation planning and strategic decision-making processes.