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Airbnb is looking for a Staff Data Scientist to join the Forecasting team, focusing on Revenue & Cancellation modeling. The role operates at the intersection of statistical forecasting and financial strategy, owning forecasting within the revenue pipeline and hierarchy, including model selection, system design, automation, and ongoing performance management.
Job Responsibility:
Own the design, operation, and evolution of end to end forecasting systems for revenue drivers, including data pipelines, model components, validation logic, and automated delivery into planning and financial workflows
Select, adapt, and maintain forecasting approaches appropriate to each problem context, balancing accuracy, stability, interpretability, and operational cost
Engage in stakeholder management, effectively communicating with various teams and levels within the organization to gather input and share insights
Drive the process of extracting actionable insights from complex data sets to influence business growth and strategic decision-making
Continuously improve forecasting infrastructure and processes to reduce manual intervention, shorten cycle time, and increase trust in outputs
Requirements:
9+ years of industry experience in a fast-paced tech environment with a BS/Master’s in a technical field related to mathematics, statistics, machine learning
3+ years of relevant experience and a PhD in similar fields
Extensive industry experience applying forecasting in production settings, with demonstrated ownership of forecasting pipelines or decision systems
Expertise in SQL and relational databases
Strong fluency in programming languages such as R, Python, and Stan
Experience building automated modeling workflows, including backtesting frameworks, model validation, scheduled retraining, and performance monitoring
Familiarity with version control, reproducibility, and data quality practices required for operating models in regulated or high visibility financial contexts
Comfort working across disciplines and making tradeoffs between statistical rigor, engineering complexity, and business constraints