Senior Data Scientist, Platform

Job Description

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: “ Our real innovation is not allowing people to book a home; it’s designing a framework to allow millions of people to trust one another. Trust is the real energy source that drives Airbnb… ” - Brian Chesky, Airbnb Co-Founder & CEO (2019) Trust is the foundation of a vibrant Airbnb community. The Content Integrity Data Science team helps build and protect that trust by ensuring that the content on Airbnb, including listings, profiles, messages and other user-generated experiences, is accurate, authentic and aligned with our policies and community standards. We partner closely with product, engineering, policy and operations teams to develop advanced AI-driven content understanding systems, scale effective human-in-the-loop workflows and proactively defend against emerging content risks, so guests and hosts can interact with confidence. The Difference You Will Make: This role is unique in that it directly improves the safety, trust, and quality of real-world user experiences by advancing Airbnb’s ability to understand, interpret and act on content at scale. You will help shape how the platform reasons about listings, profiles, messages and other user-generated content by building the next generation of Trust Content Understanding Models. The ideal candidate is a motivated and talented “full-stack” Data Scientist with strong applied ML intuition and a bias toward impact, who can own and drive forward challenging, high-visibility initiatives such as: Advance Airbnb’s content integrity capabilities by building Natural Language Processing (NLP) and LLM-based models that understand intent, policy compliance, quality and risk across listings, profiles, and user communications Develop high-performing models for detecting problematic or misleading content, including text classification, semantic similarity, information extraction and generative model-based reasoning for policy interpretation and enforcement Design and optimize human-in-the-loop Machine Learning (ML) systems for content review, labeling, escalation and continuous model improvement Build systems to detect emerging content risks and abuse patterns across regions, cohorts and surfaces using statistical, ML and representation-learning approaches Design intelligent sampling and evaluation strategies to measure rare events, policy recall, false posi

Required Skills