Data Scientist - Machine Learning

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.

Why Join Us
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.

Our Trust and Safety R&D team is fast growing and responsible for building machine learning models and systems to identify and defend internet abuse and fraud on our platform. Our mission is to protect billions of users and publishers across the globe every day. We embrace the state-of-the-art machine learning technologies and scale them to detect and improve the tremendous amount of data generated on the platform. With the continuous efforts from our team, TikTok is able to provide the best user experience and bring joy to everyone in the world.

Our Data Science team is a diverse group of problem solvers, located in Singapore, China, Canada and US, who are passionate about translating complex data into clear, actionable insights. By pioneering state-of-the-art data science techniques and fostering a culture of data-driven decision-making, we aim to unlock unprecedented growth opportunities and operational excellence.

Responsibilities
1. Design and develop data collection pipelines to gather and preprocess diverse datasets from various sources.
2. Design and develop data processing pipelines, including data labeling, data filtering, data cleaning, data visualization, data auditing, etc.
3. Implement diverse strategies to improve the quality and diversity of data.
4. Research data science theories and methodology to evaluate and improve various CV/NLP models, LLMs.
5. Design metrics framework to measure product healthiness, keep tracking of core metrics and understand root causes of metric movements.
6. You will partner with a variety of stakeholders across the algo, product, operations, policy, and engineering teams - both locally and globally.