Community Support Forecasting and Demand Planning Analyst

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 seasoned Forecasting and Demand Planning Analyst on our Community Support (CS) team. This individual is responsible for demand forecasting and long-term planning 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 and business partnership 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. - Perform 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 participate in 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. Your Expertise: - 10+ years of experience in demand forecasting, capacity planning, workforce analytics, or applied analytics. - 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 (AHT, ASA, service level, shrinkage, occupancy); experience supporting large-scale, multi-site, or global contact center environments preferred. - Advanced analytical skills with strong proficiency in Excel, Google Sheets, SQL, Python and data visualization tools such as Tableau. - Experience with WFM tools (e.g., NICE, Verint, Aspect) or planning platforms (e.g., Anaplan) preferred. - Strong business acumen with the ability to balance cost efficiency and customer experience

10+ years of experience in demand forecasting, capacity planning, workforce analytics, or applied analytics. 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 (AHT, ASA, service level, shrinkage, occupancy); experience supporting large-scale, multi-site, or global contact center environments preferred. Advanced analytical skills with proficiency in Excel, Google Sheets, SQL, Python and data visualization tools such as Tableau. Experience with WFM tools (e.g., NICE, Verint, Aspect) or planning platforms (e.g., Anaplan) preferred. Strong business acumen with the ability to balance cost efficiency and customer experience.

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. Perform 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 participate in 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.