Applied Scientist - DCC

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible. Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day. To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.

About the team
TikTok's Data Cycling Center Team focuses on building content, understanding, and providing data-driven solutions for both internal stakeholders and tools for external users. We focus on both delivering human understanding and direct content understanding helping solve large impact problems from that understanding.

About the role
The Applied Scientist will assume a hands-on and multi-faceted role within our international organization, navigating through complex data labeling processes and extensive stakeholder management to comprehend and address diverse needs. The Applied Scientist will be pivotal in amplifying the efficacy of our data labeling processes, driving both strategic and operational advancements within the team.

Responsabilities
- Advanced Project Management: Direct and coordinate projects, orchestrating efforts across a large, interdisciplinary team of researchers, product managers, and data labelers, ensuring timely and efficient execution of tasks.
- Clarification and Simplification of Product Requirements: Diligently comprehend product needs, drive clear communication, and decompose technical tasks into more manageable units, ensuring a smooth execution path.
- Machine Learning Model Development: Conceptualize and develop advanced machine learning models aimed at optimizing the output of labelers, focusing on enhancing both the speed and quality of the labeling process.
- Collaborative Experimentation and Documentation: Work in tandem with various teams to formulate meticulous experiment plans, documenting results with utmost precision, and patiently addressing inquiries from both technical and non-technical stakeholders.