Perform fraud and spam investigations using multiple data sources, identify product vulnerabilities and drive anti-abuse experiments to prevent abuse. Work with engineers and interact cross-functionally with stakeholders to improve workflows by process improvements, automation and anti-abuse system creation.
Design and refine prompts for Large Language Models (LLMs) to improve their accuracy in identifying and classifying abusive content and behavior, including prompt engineering, data labeling, and performance analysis.
Apply advanced statistical methods to datasets to understand the impact of abuse to the YouTube ecosystem. Contribute strategy and development of new workflows.
Learn technical concepts and systems and deliver results using them.
Maintain and promote quality by providing regular feedback metrics to the Global team. Manage technological solutions for streamlining quality assurance and produce training solutions.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
1 year of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
1 year of experience in managing projects and defining project scope, goals, and deliverables.
1 year of experience with one or more of the following languages: SQL, R, Python, or C++.
Preferred qualifications:
Master's degree in a quantitative discipline.
1 year of experience with machine learning systems.
Experience with collecting, managing and synthesizing datasets and information from disparate sources, statistical modeling, data mining and data analysis, and with metrics analysis, experiment design and automation.
Excellent communication skills.
