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

Tik Tok Global E-commerce Data Science team leverages rigorous data analysis and scientific methodologies to drive business growth, enhance operational efficiency, and improve the experience for users, merchants, and creators worldwide. The team conducts exploratory analyses and forward-looking insights to identify market trends and user/merchant/creator behavioral patterns, uncovering opportunities for optimization. We design and interpret A/B tests, apply causal inference methodology to support product iterations for both to C and to B products. We also build foundational data infrastructure and develop dashboards to empower product, operations, engineering, and cross-functional teams to monitor performance and identify areas for improvement. At Tik Tok, we champion a data science philosophy rooted in statistical rigor, data-driven decision-making, and scalable methodologies to support global e-commerce expansion.

We are looking for talented individuals to join us for an internship in 2026. PhD Internships at Tik Tok 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 Tik Tok 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 Tik Tok's technical online assessment.

Responsibilities:

• Conduct data analysis for Tik Tok’s global e-commerce products, covering areas such as creators & merchants, content e-commerce, promotion tools, shoptap, and transaction flow;
• Design and analyze A/B tests, evaluate product optimizations, analyze campaign performance, monitor key metric fluctuations, and assess client-side version updates;
• Analyze country-specific factors influencing product optimization and business growth, and propose actionable product improvement solutions aligned with business direction;
• Collaborate closely with product managers, operations, engineering, and algorithm teams to implement data-driven strategies and deliver measurable business impact;
• Build and maintain e-commerce product metric systems, work with data engineering teams to construct foundational data pipelines and visual dashboards, and enable business stakeholders to gain actionable insights.

Requirements:

Minimum Qualifications:

• Currently pursuing a PhD in Computer Science, Statistics, Econometrics, Mathematics or other quantitative disciplines;
• Proficient in data analysis tools and languages such as SQL, Python, or R;
• Solid foundation in statistics, with hands-on experience in A/B testing, regression analysis, and causal inference; - Strong data visualization skills and ability to clearly communicate analytical insights through dashboards and reports;
• Results-driven, highly self-motivated, and accountable; excellent team player with strong organizational and communication skills.

Preferred Qualifications:

• Experience in cross-border or international e-commerce data analysis or modeling;
• Familiarity with machine learning frameworks or predictive modeling; hands-on experience with tools such as Scikit-learn, Tensor Flow, or Py Torch;
• Experience working with big data technologies such as Hive, Spark, or Hadoop;
• Strong cross-functional communication skills.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy