Data Scientist Intern (TikTok UG-Mid Platform) - 2026 Summer (BS​/MS

Responsibilities

Team Introduction:
Tik Tok-Data Science Team is responsible for all data science and analytics related work, cooperating with all key value chains in Tik Tok, including User Growth, PGC, Content Ecosystem, Social, Creation, Product Infrastructure, Research and Science etc. The goal of the team is to generate actionable insights from data and help the stakeholders to make the right decisions. Our main tasks include metrics defining, root cause analysis, experimentation methodology, feature/strategy evaluation and exploratory analysis to find more opportunities.
We are looking for talented individuals to join us for an internship in 2026. Internships at Tik Tok aim to offer students industry exposure and hands‑on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at Tik Tok.
Internships at Tik Tok aim to provide students with hands‑on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real‑world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks.
Candidates Can Apply To a Maximum Of Two Positions And Will Be Considered For Jobs In The Order You Apply. The Application Limit Is Applicable To Tik Tok And Its Affiliates' Jobs Globally. Applications Will Be Reviewed On a Rolling Basis. We Encourage You To Apply As Early As Possible. Please State Your Availability Clearly In Your Resume (Start Date, End Date).

Successful Candidates Must Be Able To Commit To One Of The Following Summer Internship Start Dates Below:

• May 11th, 2026
• May 18th, 2026
• May 26th, 2026
• June 8th, 2026
• June 22nd, 2026

We will prioritize candidates who are able to commit to these start dates. Please state your availability clearly in your resume (Start date, End date).

• Participate in the construction and optimization of long‑term user value (LTV) models and other user behaviour prediction models.
• Use machine learning, deep learning and other techniques for data mining, and optimise user portraits and behaviour analysis.
• Design A/B tests and other causal inference methods to evaluate the model's performance and promote the implementation of the model in business scenarios.
• Process and clean massive amounts of data, build high‑quality datasets, and improve data quality.
• Collaborate with product and data engineering teams to promote the implementation of data‑driven decision‑making.

Minimum Qualifications

• Currently pursuing an undergraduate or graduate degree in a quantitative field such as mathematics, statistics, economics, or computer science.
• Analytical
  
  Skills:
  
  Previous internship or project experience in business analysis or data‑related roles, with the ability to analyse data and generate actionable insights. Experience in the ads or marketing industry is a plus.

• Technical
  
  Skills:
  
  Familiarity with Excel, SQL, Python, or R, and foundational knowledge of data visualisation tools like Tableau, Power BI, and Think‑Cell.
• Communication:
  Business‑level English proficiency with strong verbal and written skills to effectively engage with stakeholders and present findings to non‑technical audiences.
• Problem‑Solving & Teamwork:
  Strong problem‑solving abilities, attention to detail, and the capability to collaborate effectively with cross‑functional teams to implement solutions and drive results.

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【For Pay Transparency】Compensation Description (Hourly) - Campus Intern

The hourly rate range for this position in the selected city is $35- $35.

Benefits may vary depending on the nature of employment and the country work location.

• Interns have day one access to health insurance, life insurance, wellbeing benefits and more.
• Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• Interns who are not…