Lead Data Scientist, Transparency & Observability - Trust and Safety

What you'll do

• Design observability systems and metrics for the enforcement lifecycle, including monitoring, anomaly detection, alerting, and reporting.
• Build governed, AI-ready data foundations, including data quality practices, ETL pipelines, workflow orchestration, and agent-consumable data systems.
• Lead the Trust & Safety observability and AI-readiness roadmap, partnering across teams and mentoring data scientists and engineers.

What they ask for

• Experience designing scalable data ecosystems, including data modeling, governed analytics layers, and production pipelines on large-scale platforms.
• Strong SQL and Python skills; experience building data quality, monitoring, or observability solutions and organization-wide metrics frameworks.
• Understanding of AI architecture patterns such as RAG, embeddings, and knowledge systems, plus responsible AI practices applied to operational workflows.
• Bachelor's degree or above in a quantitative discipline such as Statistics, Computer Science, Mathematics, Engineering, or Economics.

Nice to have

• Experience in Trust & Safety, regulatory reporting, or compliance; familiarity with the EU Digital Services Act (DSA) or similar global frameworks.
• Experience driving program and change management across global, cross-functional teams.

Tools

Hive, Spark, SQL, Python, RAG

AI focus

Leads AI readiness across Trust & Safety, including AI-ready data foundations, agent-consumable data systems, AI architecture patterns, responsible and explainable AI adoption, and taking AI and data solutions from experimentation to reliable production.