Staff Data Scientist - Long Term Impact

Airbnb

3d•$200,000 - $242,000•Remote

About The Position

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. 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.

Requirements

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.

Nice To Haves

Publications or presentations in recognized Data Science journals/conferences is a plus.

Responsibilities

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.