Senior Data Scientist - Payments (Inference)
Airbnb was born in 2007 and has since grown to over 5 million hosts and over 2 billion guest arrivals globally. The Payments Data Science organization, part of Trust and Payments, powers the systems that move money safely and efficiently across Airbnb's global marketplace. The team focuses on payment optimization, fraud and risk mitigation, complex measurement, and regulatory compliance.
Responsibilities
Perform careful hypothesis generation, causal inference framework development, and model development/evaluation to ideate and drive payment strategies on our platform.
Focus on payments fraud mitigation and loss optimization, aiming to make our platform safer for our community.
Develop novel metrics and frameworks to measure platform/product impacts, often balancing competing tradeoffs.
Generate deep root cause investigations and long-term impact measurements.
Build and evaluate ML and agentic models to optimize guest, host, and business outcomes.
Communicate findings to stakeholders and drive impact across various teams.
Think strategically about improving and scaling brand measurement and customer insights.
Requirements
5+ years of industry experience in a quantitative analysis role with a Master’s degree in a quantitative field (math / economics / statistics), or 3+ years of experience with a PhD degree.
Strong knowledge of causal inference, experimentation, applied statistical modeling, and end-to-end ML development.
Skilled in statistical programming (Python or R) and database usage (SQL).
Demonstrated track record of owning a business or technical domain end-to-end at a prior company.
Proven ability to communicate clearly and effectively to audiences of varying technical levels.
Ability to work independently, set your own roadmap, and drive cross-functional alignment.
Familiarity with evaluating agentic or LLM-based systems (e.g., decision-quality measurement, human-in-the-loop calibration) is a plus.
Qualifications
Passionate about leading quantitative measurement efforts and bringing novel scientific approaches to drive decision-making.
Expertise in the Payments Fraud/Risk domain is a strong plus.