About this role
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 a mission-driven company dedicated to creating a world where anyone can belong anywhere. Our Community Support (CS) team is at the heart of that mission—delivering seamless, 10-star customer service experiences complemented by world-class, personalized human support that empowers hosts and guests at every step of their journey.
As a Data Scientist working on Causal Inference in CS, you will have the opportunity to collaborate with a strong team of engineers, product managers, designers and operation agents to enable personalized, fair and exceptional experience for guests & hosts using advanced causal inference analysis for Community Support.
The Difference You Will Make:
We're looking for a motivated and talented Data Scientist with strong causal inference expertise to join the Community Support Data Science team. You'll partner closely with the area's tech lead on high-impact projects spanning AI-powered products, differentiated service, and operations optimization.
The ideal candidate brings sharp applied inference intuition, a bias toward impact, and the ability to cut through ambiguity to drive clarity in complex problem spaces. You’ll work on high-impact projects like:
• Design rigorous experiments & quasi-experiments to measure the causal impact of CS product launches and drive data-informed launch decisions.
• Build causal ML models to optimize Make Goods budget allocation and maximize business impact.
• Conduct causal inference analyses to quantify the long-term effects of product changes and uncover heterogeneous treatment effects.
• Deliver strategic insights on quality-cost tradeoffs, empowering leadership to deliver the best possible support experience to our community.
A Typical Day:
• Inference & Measurement: Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance and uncover opportunities for improvement.
• Modeling: Build, evaluate and iterate on causal ML models that power high-stakes decisions, applying best practices across the full model lifecycle from feature engineering to production deployment
• Optimization: Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience and operational cost), and propose strategies to improve overall effectiveness.
• Collaborate Cross-Functionally: Build strong relationships with cross-functional partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation.
• Influence Decisions: Commu