Staff Data Scientist

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: The mission of the Core Data Science team is to ensure that we are leveraging state of the art science at Airbnb for the highest possible impact on the business and the community. We do that by building reusable tools and models that empower DS teams across the organization; and by tackling the most technically challenging, high impact data science problems the company faces. The Difference You Will Make: Estimating the causal impact of product changes is a core function of data science at Airbnb. This is typically done through short term experiments or quasi-experiments. However, ultimately we are interested not in maximizing the short term impact of what we do, but rather optimizing for the long term. Doing this well requires accurately estimating the long term impact of platform changes through frameworks that connect short-term outcomes to long-term impact. In this role, you will develop and improve our frameworks for long-term impact estimation and own the most important estimation problems you identify. We are looking for someone that can develop the science and ensure it is fitted to Airbnb's problem space and stakeholder needs. Success is better long-term impact estimation in practice, not just in theory. A Typical Day: Develop causal estimates for long-term impact of short-term metric movements. Build frameworks for estimating how the impact of product changes evolves over time. Create frameworks and tooling to evaluate the heterogeneous impact of product changes Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts. Your Expertise: Advanced degree in a quantitative field, e.g. Operations Research, Economics, Mathematics, Physics Proven ability to design analytical solutions to complex problems with real world applications Familiarity with recent developments in causal inference 6+ years (with Ph.D) or 8+ yrs (with Masters) of experience of working or doing research in one or more experimentation / causal inference domains Strong programming (R, Python / Scala / Java / C++ or equivalent) skills Versatility to communicate clearly with both technical and non-technical audiences. Publications or presentations in recognized Data Science journals/conferences is a plus. 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. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: View email address on click.appcast.io . Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.