Senior Manager Global Capacity and Operational Planning (Community Support)

The Community You Will Join:

The Community Support org handles tens of millions of interactions yearly, engaging with Airbnb customers by phone, messaging, chat, or social media channels. The group handles hundreds of issues across categories including Cancellations, Account Issues, Refunds, Payments, Reservations, Extenuating Circumstances, Booking & Listing issues, Safety & Claims. The organization is globally distributed with offices in San Francisco, Dublin, Montreal, Seattle, Singapore, Manila, Gurgaon and an extensive partner network serving all regions.

The Difference You Will Make:

We are looking for a Senior Manager for Demand Planning on our Community Support (CS) team. This leader is responsible for leading demand forecasting and long-term planning strategy across multi-channel contact center operations for our Global Operations team. This role ensures the organization has the right resources, at the right time, with the right skills to meet service level objectives while optimizing cost and productivity.

The role combines planning, advanced analytics, business partnership, and people leadership to drive scalable, data-driven workforce decisions in a complex, fast-changing environment.

A Typical Day:

Demand Forecasting & Statistical Modeling

• Own short-term, mid-term, and long-term demand forecasting across all Global Operations teams and channels (phone, messaging, email, back-office etc.).
• Design, develop, and maintain statistically robust demand forecasting models using time series and machine learning techniques (e.g., exponential smoothing, ARIMA, regression-based models etc.).
• Perform trend, seasonality, and variance decomposition; detect structural breaks, outliers, and demand anomalies.
• Quantify forecast uncertainty through confidence intervals, error distributions, and bias analysis.
• Lead scenario modeling for peak demand periods, product launches, growth initiatives, and unplanned demand events.
• Continuously assess model performance using statistical accuracy metrics (MAPE, RMSE, MAE, bias etc).