Data Scientist

• 2+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (statistics, economics, computer science, etc.), or PhD in relevant fields.
• State-of-the-art knowledge of AI/ML models
• Hands-on experience building, evaluating and deploying NLP and LLM-based solutions
• Strong fluency in Python and SQL, experience with Tensorflow, PyTorch, Spark, Airflow and data warehouse
• Proven ability to communicate clearly and effectively to audiences of varying technical levels. Excellent project management, communication and collaboration skills
• Working knowledge of causal inference
• Proven mix of strong intellectual curiosity with high level of pragmatism and engagement with the technical community. Publications or presentations in recognized journals/conferences is a plus

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $151,000-$175,000 USD Go ad-free with Premium ×, Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $151,000-$175,000 USD

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 Trust 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 sits at the heart of some of Airbnb’s most consequential data science challenges, where rigorous statistical thinking and applied ML directly shape platform outcomes. You will own high-visibility initiatives that require both technical depth and strong business judgment - work that is visible to leadership and has measurable impact on Airbnb’s users and bottom line.

The ideal candidate is a motivated and talented Data Scientist with strong applied machine learning intuition, a bias toward impact and the ability to navigate ambiguity and drive clarity in complex problem spaces.

A Typical Day:

• Artificial Intelligence / Machine Learning: Build and deploy production AI/ML systems for trust integrity, including feature engineering, model development and evaluation, thresholding, error analysis and end-to-end model lifecycle management. This includes working with NLP and LLM-based models in real production settings.
• Inference: Partner with inference data scientists to conduct rigorous quantitative analyses, applying working knowledge of causal inference to interpret results, assess impact, and identify gaps and opportunities to improve content quality and trust outcomes.
• Communication & Collaboration: Deliver clear, well-structured research findings with compelling data visualizations and rigorous analysis to drive alignment and action with cross-functional partners in product, engineering and operations