Tech Stack

Description

You will own the design, operation, and evolution of end-to-end forecasting systems for revenue drivers at Airbnb, including data pipelines, model components, and automated delivery into financial workflows. You'll select and maintain forecasting approaches that balance accuracy, stability, and interpretability, while engaging with stakeholders to extract actionable insights that drive business growth.

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, or 6+ 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, along with 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.

Responsibilities

• 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.