Global Head of Data Science - Trust and Safety

About the role

TikTok's Trust & Safety Data Science team provides strategic and analytical support to make the platform the most trusted for users and creators, partnering with Policy, Risk Prevention & Response, Transparency Reporting and other functions. The Global Head of Data Science leads the global strategy, builds and develops high-performing teams across regions, and turns business challenges into scalable data-driven solutions.

Key responsibilities

• Lead and define the global Data Science strategy for Trust & Safety, aligned with business priorities and long-term objectives.
• Build, develop and lead Data Scientist teams across multiple regions.
• Partner with Product, Engineering, Policy, Operations and executive leadership on data-driven solutions.
• Define and evolve the measurement framework, including north-star metrics, experimentation strategy and operational KPIs.
• Drive advanced analytics, machine learning, forecasting and optimization models that improve decision-making.
• Improve data quality, governance, observability and measurement consistency.
• Deliver executive-level insights and champion an AI-first approach using automation, GenAI and predictive analytics.
• Foster technical excellence, innovation and collaboration, and represent the function as a strategic partner to global leadership.

Required profile

• Bachelor's degree in a quantitative discipline or equivalent practical experience.
• 5 years of experience in Data Science, Machine Learning, Analytics or a related field.
• 5 years leading and developing global Data Science or Analytics organizations in a technology company.
• Demonstrated experience defining and executing organization-wide data strategies.
• Preferred: Master's or PhD, experience leading Trust & Safety, Risk, Fraud or Security data science teams, and experience with globally distributed teams.

Required skills

• Statistical analysis, experimentation, machine learning and large-scale data analytics.
• Large-scale distributed data platforms and tools such as SQL, Python, Spark, Hadoop and cloud data platforms.
• Preferred: experimentation frameworks, causal inference, GenAI and LLM-enabled products.