Lead Data Scientist, Transparency & Observability - Trust and Safety

The Transparency & Observability Data Science team builds the data, metrics, and analytical capabilities that help us understand how our enforcement systems perform. Our work supports both internal decision-making and external transparency reporting, while improving the reliability, explainability, and quality of Trust & Safety data.

As a Lead Data Scientist on this team, you will be the technical architect for how Trust & Safety measures, monitors, and explains itself. You will design the systems and data foundations behind our metrics, observability, and transparency reporting, and serve as the AI and data architecture lead who drives AI readiness across Trust & Safety.

Responsibilities: - Architect Transparency & Observability Systems: - Establish the Transparency & Observability architecture for Trust & Safety, covering metrics, data pipelines, monitoring, and reporting. - Design end-to-end observability for the enforcement lifecycle, from detection and review to actions and outcomes, so that every enforcement decision can be traced, measured, and explained. - Set standards for metric definitions, anomaly detection, and alerting, and make sure Trust & Safety reports one consistent view of its enforcement. - Build Governed, AI-Ready Data Foundations: - Set up data quality, lineage, and governance practices (SLAs, validation, reconciliation, documentation) so that Trust & Safety data is accurate, timely, and audit-ready for regulatory reporting. - Design and oversee ETL pipelines and workflow orchestration that turn scattered inputs into reliable, well-documented data products. - Build data systems designed for agent consumption across Trust & Safety, covering canonical data models, semantic layers, intelligent workflows, and evals. - Drive AI Readiness and Technical Leadership Across Trust & Safety: - Partner with Product, Engineering, Policy, Operations, Legal, and Comms to find high-impact opportunities where AI and data can improve operational efficiency and safety outcomes, and take solutions from experimentation to reliable production. - Own the long-term technical roadmap for Trust & Safety observability and AI readiness, and lead the program and change management needed to land it across teams. - Create reusable architecture principles, reference architectures, and best practices for responsible, explainable AI adoption across Trust & Safety teams, and mentor data scientists and engineers in applying them.

Minimum Qualification(s): - 5+ years of experience in Data Science, Data Engineering, Data Architecture, Analytics, or a related quantitative field, including technical leadership of cross-team initiatives. - A track record of designing scalable data ecosystems: data modeling, governed analytics layers, and production pipelines on large-scale platforms (e.g. Hive, Spark). - Strong SQL and Python skills. - Experience building data quality, monitoring, or observability solutions, and designing metrics frameworks used across organizations. - A solid understanding of AI architecture patterns (e.g. RAG, embeddings, knowledge systems) and responsible AI practices, applied to operational workflows. - Ability to translate ambiguous business problems into technical strategy, and to influence both technical and non-technical stakeholders, including leadership. - Bachelor's degree or above in a quantitative discipline such as Statistics, Computer Science, Mathematics, Engineering, or Economics.

Preferred Qualification(s): - Experience in Trust & Safety, regulatory reporting, or compliance domains. - Familiarity with transparency and regulatory requirements such as the EU Digital Services Act (DSA) and similar global frameworks. - Experience driving program and change management across global, cross-functional teams.