Senior Data Scientist - Payments (Inference)

Airbnb · Remote - USARemotesenior

In short

▸Liderazgo en análisis causal y modelos de inferencia para mejorar la detección de fraude en pagos globales.
▸Día a día: construir marcos de medición, evaluar modelos ML/IA y generar insights para decisiones estratégicas en pago.
▸Destacado: impacto directo en la seguridad financiera global de Airbnb, con participación en decisiones ejecutivas.

Fluency in English required for collaboration across global teams.

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What they ask for

✓Experiencia sólida en inferencia causal y diseño de experimentos en entornos de mercado dual.
✓Dominio en modelado estadístico y evaluación robusta de modelos ML/AI.
✓Habilidades para desarrollar métricas innovadoras que equilibren tradeoffs complejos.
✓Capacidad para comunicar hallazgos técnicos a audiencias no técnicas (ejecutivos, product managers).
✓Experiencia comprobada con sistemas de pagos, riesgo o fraude en entornos a escala global.
✓Formación avanzada en estadística, ciencia de datos, ingeniería o campos relacionados.

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Causal InferenceExperimental DesignEconometric RegressionsQuasi-Experimental MethodsMachine LearningModel EvaluationPredictive ModelingUser Behavior SegmentationAgentic SystemsLLM-based Systems

Who should you write to at Airbnb?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

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 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. 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 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. A Typical Day: • 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

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