Data Scientist Intern (TikTok Shop User Product DS) - 2026 Start (PhD)

TikTok’s Global E‑commerce Data Science team powers TikTok Shop by building analytical frameworks and product metrics to drive growth, improve operational efficiency, and optimize the buyer, seller, and creator experiences across regions. They identify trends, user or merchant behavior patterns, and opportunities for business optimization through exploratory analysis and proactive insights generation. The team designs and interprets A/B tests and causal experiments to inform product improvements for user growth, transaction funnels, and feature adoption. They also develop dashboards and data pipelines that enable cross‑functional teams—Product, Operations, and Engineering—to monitor product performance and implement data-driven changes easily. TikTok sets a culture focused on statistical rigor, ownership, and scalable machine learning methods in e-commerce decision-making at global scale.

We are looking for talented individuals to join us for an internship in 2026*. PhD Internships at TikTok aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at TikTok provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:
- May 11th, 2026
- May 18th, 2026
- May 26th, 2026
- June 8th, 2026
- June 22nd, 2026

Online Assessment
Candidates who pass resume screening will be invited to participate in TikTok's technical online assessment

Responsibilities:
- Analyze product usage and engagement data (e.g., retention, conversion, feature adoption) to identify trends and actionable insights;
- Support design and evaluation of A/B tests or feature experiments; compute lift, assess statistical significance, and help interpret outcomes;
- Conduct exploratory data analysis using SQL and Python to detect patterns, anomalies, or feature-driven behaviors;
- Create dashboards, visualizations, and summary reports to communicate findings to product, engineering, and design teams;
- Collaborate cross-functionally with product managers and engineers to implement metrics and iterate on insights; document analysis and methodologies.