Senior Manager Global Capacity and Operational Planning (Community Support)

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: 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). - Establish model governance standards, including documentation, validation, back-testing, and post-mortem analysis. - Research, prototype, and implement new forecasting and optimization techniques as business needs evolve. - Perform scenario planning and sensitivity analysis to quantify trade-offs between service levels, cost, and utilization. Advanced Analytics - In partnership with the Analytics and Data Engineering team, design and build scalable planning data pipelines, dashboards, and automate forecasting and capacity models to improve scalability, repeatability, and timeliness. - Use SQL and Python to extract, transform, and analyze large-scale operational datasets to monitor forecast accuracy, capacity gaps, utilization, and operational risk. Decision Support & Stakeholder Communication - Present forecast assumptions, methodologies, risks, trade-offs, and recommendations in clear, executive-ready formats and act as a trusted advisor to senior leadership and lead demand/capacity planning discussions in cross-functional forums. - Align cross-functional stakeholders (Delivery, Product, Finance, HR, and others) to embed planning outputs into execution and operational decision-making. - Identify and implement process improvements, automation, and best practices in the demand and capacity planning area to optimize cost while maintaining or improving customer experience and service-level outcomes. People Leadership - Lead, mentor, and develop planning analysts in quantitative methods, statistical rigor, and coding best practices. - Review and approve forecasting and capacity models for accuracy, scalability, and business relevance. - Set clear goals, high performance expectations, and career development paths for the team. - Foster a culture of data-driven decision-making, accountability, and continuous improvement. Your Expertise: - 12+ years of experience in demand forecasting, capacity planning, workforce analytics, or applied analytics. - 5+ years of people management experience. - Bachelor’s degree in Mathematics, Statistics, Operations Research, Engineering, Economics, Data Science, or a related quantitative field. - Strong foundation in probability, statistics, and optimization. - Hands-on experience building and validating forecasting models (time series analysis, exponential smoothing, ARIMA, regression, hypothesis testing) and capacity models (Erlang, queueing theory, service-level and utilization modeling). - Strong understanding of contact center metrics

12+ years of experience in demand forecasting, capacity planning, workforce analytics, or applied analytics. 5+ years of people management experience. Bachelor’s degree in Mathematics, Statistics, Operations Research, Engineering, Economics, Data Science, or a related quantitative field. Strong foundation in probability, statistics, and optimization. Hands-on experience building and validating forecasting models (time series analysis, exponential smoothing, ARIMA, regression, hypothesis testing) and capacity models (Erlang, queueing theory, service-level and utilization modeling). Strong understanding of contact center metrics.

Lead demand forecasting and long-term planning strategy across multi-channel contact center operations for Global Operations. Design, develop, and maintain statistically robust demand forecasting models (time series, ML techniques) for short-, mid-, and long-term horizons across all channels. Conduct trend, seasonality, variance analyses; quantify forecast uncertainty; lead scenario modeling for peak periods and new initiatives; monitor model performance with accuracy metrics; establish governance for models including validation and post-mortems. Partner with Analytics and Data Engineering to design scalable planning data pipelines, dashboards, and automated forecasting. Use SQL and Python to analyze large datasets for forecast accuracy, capacity gaps, utilization, and risk. Present forecasting methodologies, assumptions, risks, and recommendations to senior leadership; align cross-functional stakeholders to embed planning outputs into execution. Lead, mentor, and develop planning analysts; set goals and career development paths; foster a data-driven culture. Identify process improvements and automation in demand and capacity planning to optimize cost while preserving customer experience and SLAs.