Data Scientist - Inference, Community Support

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: Communicate learnings to leaders and stakeholders in a clear, compelling manner that drives informed, data-driven decision-making. - Empowerment: Think strategically about how to scale and evolve data science capabilities within your domain, contributing to the long-term vision for how science drives platform outcomes. Your Expertise: - 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. - Strong knowledge of causal inference and experimental design. - Strong knowledge of Bayesian modeling and statistical inference. - Hands-on experience building and deploying statistical or ML models in production environments. - Skilled in statistical programming (Python/R) and database usage (SQL). - Proven ability to communicate clearly and effectively to audiences of varying technical levels. - Ability to translate complex findings into compelling narratives that drive impact. - Excellent project management, communication and collaboration skills. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

MS/PhD in statistics, economics, computer science, or a related quantitative field; 2+ years of industry experience in a quantitative analysis role; strong knowledge of causal inference and experimental design; strong knowledge of Bayesian modeling; hands-on experience building and deploying statistical or ML models in production; proficient in Python or R and SQL.

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 Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation. Influence Decisions: Communicate learnings to leaders and stakeholders in a clear, compelling manner that drives informed, data-driven decision-making. Empowerment: Think strategically about how to scale and evolve data science capabilities within your domain, contributing to the long-term vision for how science drives platform outcomes.