Community Support Forecasting and Demand Planning Analyst
A
AirbnbContact center operations
US - Remote EligibleFull-TimeSenior
Salary$168,000 — $210,000 USD
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Job Details
- Experience
- 10+ years of experience in demand forecasting, capacity planning, workforce analytics, or applied analytics.
- Required Skills
- PythonSQLMicrosoft ExcelGoogle Sheets
Requirements
- 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, including time series analysis, exponential smoothing, ARIMA, regression, and hypothesis testing.
- Experience building capacity models using Erlang, queueing theory, service-level, and utilization modeling.
- Strong understanding of contact center metrics, including AHT, ASA, service level, shrinkage, and occupancy.
- Advanced proficiency in Excel, Google Sheets, SQL, Python, and data visualization tools such as Tableau.
- Ability to balance cost efficiency with customer experience outcomes and explain complex analytical concepts to non-technical audiences.
- Experience supporting large-scale, multi-site, or global contact center environments preferred.
- Experience with WFM tools such as NICE, Verint, or Aspect, or planning platforms such as Anaplan, preferred.
- Comfort operating in fast-paced, ambiguous, and dynamic environments.
Responsibilities
- Own short-, mid-, and long-term demand forecasting across Global Operations teams and channels.
- Design, develop, and maintain statistical forecasting models using time series and machine learning techniques.
- Analyze trends, seasonality, variance, forecast uncertainty, model performance, and demand anomalies.
- Model scenarios and sensitivities for peak periods, product launches, growth initiatives, and unplanned demand events.
- Establish model governance, including documentation, validation, back-testing, and post-mortem analysis.
- Partner with Analytics and Data Engineering to build planning data pipelines, dashboards, and automated forecasting and capacity models.
- Use SQL and Python to analyze operational datasets and monitor forecast accuracy, capacity gaps, utilization, and operational risk.
- Present forecast assumptions, methodologies, risks, trade-offs, and recommendations to senior leadership.
- Align Delivery, Product, Finance, HR, and other stakeholders to embed planning outputs in operational decisions.
- Implement process improvements, automation, and best practices in demand and capacity planning.
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