Senior Data Scientist, Trust (Inference)

Airbnb · Remote - USARemotosenior

En corto

▸Científico de datos senior enfocado en detectar fraude y riesgo en la plataforma de Airbnb.
▸Día a día: construir modelos estadísticos, evaluar políticas de confianza y liderar experimentos complejos con impacto directo en la seguridad del usuario.
▸Destacado: trabajo con alta visibilidad en decisiones estratégicas que afectan a millones de anfitriones y huéspedes.

🌐 Proficiency in English is required for collaboration across global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia sólida en ciencia de datos con enfoque en inferencia causal.
✓Habilidades avanzadas en modelado estadístico y Bayesian.
✓Capacidad para diseñar y analizar experimentos en entornos no estándar (sin A/B testing clásico).
✓Experiencia en detección de fraudes, manipulación de contenido o riesgo de cuentas.
✓Excelentes habilidades de comunicación para audiencias técnicas y no técnicas.
✓Dominio del análisis de datos con impacto en producto y política.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonRSQLBayesian modelingCausal inferenceExperimentationStatistical modelingData visualizationMachine learningA/B testing

🎯 ¿A quién escribirle en Airbnb?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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: “ Our real innovation is not allowing people to book a home; it’s designing a framework to allow millions of people to trust one another. Trust is the real energy source that drives Airbnb… ” - Brian Chesky, Airbnb Co-Founder & CEO (2019) Data science is the engine behind Airbnb's most impactful decisions. The Platform Data Science team accelerates product evolution and business outcomes by combining scientific rigor with deep domain expertise, spanning experimentation, machine learning, causal inference and scalable intelligence. We partner closely with product, engineering, policy, and operations teams across Trust to detect and defend against the adversarial behavior that threatens guest and host trust: fraudulent listings and fake inventory, review and content manipulation, account takeover, and other bad-actor activity on the platform. Whether measuring the impact of a new listing integrity defense, modeling risk at the listing or account level, or evaluating the effectiveness of an enforcement policy, our work helps guests and hosts experience an Airbnb that is safer, smarter and more personalized. The Difference You Will Make: This role sits at the heart of some of Airbnb's most consequential data science challenges, where rigorous statistical thinking and applied ML directly shape platform outcomes. You will own high-visibility initiatives that require both technical depth and strong business judgment - work that is visible to leadership and has measurable impact on Airbnb's users and bottom line. A Typical Day: The ideal candidate is a technically exceptional and strategically minded Data Scientist who can navigate ambiguity, drive clarity in complex problem spaces and influence product and policy decisions. You will own and drive initiatives such as: • Inference & Measurement: Design and implement measurement and evaluation frameworks to understand the impact of Trust defense, and uncover opportunities for improvement • Experimentation: Collaborating closely with cross-functional partners, lead the design and analysis of experiments and quasi-experiments in environments where standard A/B testing is challenging. • Modeling : Build and iterate on statistical and Bayesian models that quantify risk, estimate treatment effects, and surface measurement gaps; provide causal interpretation of signals surfaced by ML systems • Insights and Strategy: Generate deep insights on the effectiveness of Trust defenses and translate them into clear strategic recommendations for product roadmap and policy decisions • Communication & Collaboration: Deliver robust, leadership-ready research with rigorous analysis, compelling data visualizations, and clear narratives; effectively communicate complex methodological tradeoffs to both technical and non-technical audiences across product, engineering, policy and operations • Empowerment:</st

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