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# Data Scientist - Multimedia

- **Company:** Tiktok Inc
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $95,000.0 - $216,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Data Mining, Apache Hive, Mobile Application Software, Python (Programming Language), Standard Sql, Data Streaming, Apache Spark, Data Analytics, Data Management
- **Published:** October 2, 2026
- **Apply:** https://www.themuse.com/jobs/tiktok/data-scientist-multimedia

## About the Role

Experience in data science roles, preferably working on mobile application performance analysis and user experience optimization

 - Experience working with creation-oriented user behavior
 - Proficient in SQL and Python; experience with big data tools such as Hive or Spark.
 - Strong background in A/B testing, metric design, and causal inference.

Preferred Qualifications

 - Experience within the entertainment industry, streaming, social applications
 - Experience working in a fast-paced environment with rapid product iterations

## Description

Team Introduction
The Multimedia Data Scientist team at TikTok focuses on optimizing the app experience for users through data-driven solutions. Collaborating with various teams to enhance content creation journeys, utilizing statistical models and data mining techniques to identify opportunities for personalized user publishing experiences.

Responsibilities

 - Develop the experience metric system and the corresponding monitoring capability across different stages of creation
 - Conduct experimentation, define launch standards and provide suggestions on experiment iterations
 - Quantify the impact of creation tool performance on user behavior, such as publish rate, retention, and engagement
 - Use data mining for understanding user needs and build models that enable personalized publishing experience
 - Work with various partners on landing solutions that meet the needs for evolving products
 - Build systemic problem solving capability and research on cutting-edge methodologies