Data Scientist, Community Support (Inference)

<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><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Community You Will Join: </strong></span></p> <p>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.</p> <p>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 &amp; hosts using advanced causal inference analysis for Community Support.</p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Difference You Will Make:</strong></span></p> <p>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.</p> <p>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:</p> <ul> <li>Design rigorous experiments &amp; quasi-experiments to measure the causal impact of CS product launches and drive data-informed launch decisions.</li> <li>Build causal ML models to optimize Make Goods budget allocation and maximize business impact.</li> <li>Conduct causal inference analyses to quantify the long-term effects of product changes and uncover heterogeneous treatment effects.</li> <li>Deliver strategic insights on quality-cost tradeoffs, empowering leadership to deliver the best possible support experience to our community.</li> </ul> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>A Typical Day:&nbsp;</strong></span></p> <ul> <li><strong>Inference &amp; Measurement:</strong> Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance and uncover opportunities for improvement.</li> <li><strong>Modeling:</strong> 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</li> <li><strong>Optimization:</strong> Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience and operational cost), and propose strategies to improve overall effectiveness.</li> <li><strong>Collaborate Cross-Functionally: </strong>Build strong relationships with cross-functional partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation.</li> <li><strong>Influence Decisions:</strong> Communicate learnings to leaders and stakeholders in a clear, compelling manner that drives informed, data-driven decision-making.</li> <li><strong>Empowerment:</strong> 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.</li> </ul> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>Your Expertise:</strong></span></p> <ul> <li>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.</li> <li>Strong knowledge of causal inference and experimental design.</li> <li>Strong knowledge of Bayesian modeling and statistical inference.</li> <l