Senior Data Scientist - Payments (Inference)

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

You will join the Payments Data Science organization, which sits at the intersection of Trust and Payments and powers the systems that move money safely and efficiently across Airbnbs global marketplace

The team spans payment optimization for guests and hosts, fraud and risk mitigation, complex measurement, and regulatory compliance We partner directly with Payments product and engineering leadership, Finance, and Trust to ensure every transaction is fast, safe, and compliant at global scale

Our work directly shapes decisions made by senior leaders, including Payments executive leadership, and requires a rigorous, evidence-based approach to every recommendation we make

Our Data Science team enables this mission by providing reliable measurement frameworks to deliver robust data insights, build and enable state-of-the-art data products/models, and provide actionable and reliable business guidance.

The Difference You Will Make

We are looking for a passionate data scientist to lead quantitative measurement efforts and bring novel scientific approaches to drive decision making across our platform’s payment experience

This data scientist will perform careful hypothesis generation, causal inference framework development, and model development/evaluation to ideate and drive payment strategies on our platform

This role will have a particular focus on payments fraud mitigation and loss optimization, with the goal of making our platform safer for our community.

Our Data Scientists have a deep understanding of causal framework development, statistical analysis, machine learning model development and evaluation strategies, and the complications of running experiment/quasi-experimental methods in a two-sided marketplace

They have keen business sense and are able to develop novel solutions to fraud and risk problems that dont have an established playbook and utilize their findings to communicate across a wide range of partners to drive our data & product roadmaps

They are not only the trusted data expert on their team, but also a storyteller.

Examples of projects you may work on include, development of novel metrics and frameworks that can efficiently measure outcomes