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Data Science Intern

Airbnb
United StatesRemotevia LinkedIn
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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: Airbnb is seeking a (PhD) Data Science Intern to join the Core Data Science team for our 2026 Summer Intern Program from June 1, 2026 - August 21, 2026. The Data Science organization aims to be a business multiplier by solving complex and technical questions that underlie the foundations of Airbnb’s business through a mix of data research, modeling, and empirical methods. Core Data Science at Airbnb aims to innovate and apply deep scientific approaches to solve critical problems for the the Airbnb platform and it's users. The team aims to accelerate scalable scientific innovation through bringing new methods and technologies to Airbnb problems across the company. The Difference You Will Make: During the internship, you’ll be integrated into the Trip Intelligence working group at Airbnb. Airbnb's product is the Trip, and the Trip Intelligence working seeks to transform our understanding of what happens in the real world on trips. The primary goal of your role is to improve the quality of our GSAT model, which infers Guest satisfaction with their trip experience through applications of AI to a variety of data sources. Key Responsibilities Includes: - Conducting literature reviews to identify ways in which active learning can be integrated into our current model. - Investigating innovative approaches to improving labeling for training and evaluation datasets. - Implementing an active learning pipeline for our existing model and analyzing the performance - Investigating other approaches to improving label quality (e.g. LLM labeling agents). This internship offers a unique oppor
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