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 Airbnb’s 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
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 don’t 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 (often balancing competing tradeoffs), generating deep root cause investigations and long term impact measurements, and building/evaluating ML and agentic models to optimize guest, host, and business outcomes
Inference: Develop and apply causal inference methods, including experimental, econometric regressions, and quasi-experimental methods to measure a wide-range of platform/product impacts
AI/ML: Build methods for robust evaluation of ML/AI model efficiency and performance. Ability to identify use-cases for and develop predictive models to classify, segment, and interpret our users’ behavior. Support evaluation and optimization of agentic and LLM-based systems
Optimization: Develop methodologies to explore/simulate the impact of new interventions and develop data products to optimize product/operational strategies
Communication: Deliver robust research reports and effective data visualizations. Collaborate with and present to stakeholders to identify opportunities and communicate findings, and drive impact
Empowerment: Think strategically about opportunities to improve and scale our brand measurement and customer insights
Benefits
Paid volunteer time
Health food and snacks
Generous parental and family leave
Learning and development
Annual travel and experiences credit- Ability to work independently, set your own roadmap, and drive cross-functional alignment
Payments Fraud/Risk Domain expertise is a strong plus
Proven ability to communicate clearly and effectively to audiences of varying technical levels
Familiarity with evaluating agentic or LLM-based systems (e.g., decision-quality measurement, human-in-the-loop calibration) is a plus
Skilled in statistical programming (Python or R) and database usage (SQL)
5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (math / economics / statistics, and etc.), or 3+ years of experience with a Phd degree
Strong knowledge of causal inference, experimentation, applied statistical modeling, and end-to-end ML development
Demonstrated track record of owning a business or technical domain end-to-end at a prior company: setting your own roadmap, being the accountable expert others escalate to, and driving a problem to resolution
