Senior Data Scientist, Guest Travel Insurance (Algorithms)

<div class="content-intro"><p><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">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.</span></p></div><p><strong>The Community You Will Join:</strong></p> <p>Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. Travel should feel safe—and AirCover is how we deliver on that promise. Through Guest Travel Insurance (GTI), we offer guests peace of mind at the moment of booking and throughout their trip. As a Data Scientist on AirCover, you’ll work at the intersection of insurance, personalization, and machine learning—building intelligent systems that help the right guest discover the right coverage at the right moment. You’ll join a tight-knit, high-output DS team that runs one of Airbnb’s most experiment-dense personalization roadmaps, partnering daily with product, engineering, operations, and legal to ship work that directly affects guest trust and revenue.</p> <p><strong>The Difference You Will Make:</strong></p> <p>We’re looking for a machine learning expert who is excited to own hard problems end-to-end—from prototype to production. You’ll have direct scope to contribute and lead across:</p> <ul> <li>Package personalization &amp; ML-based recommendation: Evolve rule-based guest segmentation into a full ML recommendation system that surfaces the right insurance (e.g., trip cancellation, accidental damage coverage, on-trip protection) to each guest based on purchase intent, trip attributes, listing signals, and user history.</li> <li>Content personalization: Build models that rank and select benefit messaging for each guest—deciding which coverages to highlight, in what order, and with what framing—drawing on learnings from segmentation experiments and LLM-assisted content prototyping.</li> <li>Intent modeling: Develop and productionize ML models (from gradient-boosted trees to deep learning) that predict a guest’s likelihood to value specific coverages, using structured booking data and unstructured signals.</li> <li>Journey understanding and optimization: Leverage reinforcement learning to personalize across user journey, with understanding on user preferences on entry point, price, notification frequency, and trip characteristics</li> <li>High-velocity experimentation: Design and run adaptive experiments to maximize learning within tight traffic constraints; sequence ERFs strategically to keep the personalization roadmap moving.&l