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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 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.
Job Responsibility
Lead demand forecasting and long-term planning strategy across multi-channel contact center operations for Global Operations team
Own short-term, mid-term, and long-term demand forecasting across all Global Operations teams and channels
Design, develop, and maintain statistically robust demand forecasting models using time series and machine learning techniques
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
Establish model governance standards
Research, prototype, and implement new forecasting and optimization techniques
Perform scenario planning and sensitivity analysis to quantify trade-offs between service levels, cost, and utilization
In partnership with Analytics and Data Engineering team, design and build scalable planning data pipelines, dashboards, and automate forecasting and capacity models
Use SQL and Python to extract, transform, and analyze large-scale operational datasets
Present forecast assumptions, methodologies, risks, trade-offs, and recommendations in clear, executive-ready formats
Align cross-functional stakeholders to embed planning outputs into execution and operational decision-making
Identify and implement process improvements, automation, and best practices in demand and capacity planning
Lead, mentor, and develop planning analysts
Review and approve forecasting and capacity models
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
Requirements
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)
Advanced analytical skills with strong proficiency in Excel, Google Sheets, SQL, Python and data visualization tools such as Tableau
Strong business acumen with the ability to balance cost efficiency and customer experience outcomes
Excellent communication, executive presentation, and stakeholder influence skills
ability to explain complex analytical concepts to non-technical audiences
Comfortable operating in fast-paced, ambiguous, and highly dynamic environments
Strong understanding of contact center metrics (AHT, ASA, service level, shrinkage, occupancy)
Nice to have
Experience supporting large-scale, multi-site, or global contact center environments
Experience with WFM tools (e.g., NICE, Verint, Aspect) or planning platforms (e.g., Anaplan)