TikTok LIVE - Content Quality Analyst (Turkish Speaking)

About the Team
The livestream industry has seen tremendous growth in recent years and has become the next growth driver for TikTok global business. It also brings great joy to users globally and it creates a new channel for our creators to show themselves.

We are seeking a Content Quality Analyst to join our team to lead quality initiatives in content moderation—leveraging both human labeling and machine learning models, including Large Language Models (LLMs). This role plays a critical part in upholding content quality across Tiktok Live platform. The ideal candidate will be deeply involved in training AI models, analyzing moderation data to extract actionable insights, resolving complex content scenarios, and working closely with partner teams to ensure effective issue resolution. Success in this role requires strong analytical thinking, high ownership, and the ability to collaborate across cross-functional teams. A keen eye for detail and a proactive mindset toward challenging existing processes and driving continuous improvement are essential.

Detailed responsibilities include:
1. Ensure high-quality content benchmarking across BPO teams, support the setup, onboarding, and training of new BPO teams. Manage policy clarifications, calibrations, and arbitration processes. Analyze BPO team performance to identify knowledge gaps and systemic issues.
2. Handle content labeling for specific queues based on cross-functional business requirements, analyze labeled content to identify trends and provide insights to project teams and independently manage quality evaluation initiatives, delivering actionable analytical insights.
3. Train models using large datasets of labeled content to improve decision-making accuracy. Enhance model capabilities through iterative training and reinforcement learning, assist in data preparation, cleaning, and structuring for training purposes. Improve model accuracy by testing and fine-tuning, highlight the algorithm team with supporting examples for any potential gaps in LLM decision making draft, revise, and quality-check content to explore and enhance the synergy between human input and data in LLM training.
4. Analyze moderation and labeling data from multiple queues to derive a process or market specific trend/correlation. Arrange supporting data points for impact analysis and solution planning.
5. Custodian of all quality specific artifacts for the content moderation scope, generating creative solutions, including the use of technology and tools, to enhance the quality of both individual and team outputs.