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Salary: USD 132,400 - 222,600 / annual
Zillow is seeking an Applied Scientist to join its dynamic forecasting team, leveraging a comprehensive database of over 100 million homes, transactions, and users to predict and analyze housing market trends.
You will specialize in traditional econometrics and machine learning with a focus on time series and hierarchical forecasting. Key responsibilities include:
• Collaborative Forecast Development: Work alongside other applied scientists, data scientists, and economists to develop hierarchical forecasts for the housing market at various regional levels.
• Feature Extraction: Identify economic and demographic driving forces of housing market growth across regions and extract features that predict housing market trends 1 to 2 years in the future.
• Model Deployment: Collaborate closely with the engineering team to deploy models into production environments.
• Forecast Production: Contribute to monthly forecast production, communicating forecast performance and the housing market outlook to business partners and senior leadership.
• Scenario Modeling: Develop scenario models that capture a wide range of housing market outcomes and contribute to a company-wide stress-testing framework.
Zillow is the most-visited real estate platform in the U.S., helping people navigate buying, selling, financing, and renting. The company emphasizes distributed work flexibility through its Cloud HQ initiative, enabling employees to work from wherever they're most productive. Zillow was recognized as one of FORTUNE's 100 Best Companies to Work For in 2026 and included on TIME's America's Best Companies 2026 list.
REQUIREMENTS:
• 2+ years of proven experience working in time series/spatial forecasting
• Master's or PhD degree in Mathematics, Statistics, Economics, Econometrics, Physics, Earth Sciences, or a related scientific field
• Strong foundation in traditional econometric methods and modern machine learning techniques
• Proficiency in data cleaning, preprocessing, and feature engineering for time series data
• Experience with large-scale datasets and familiarity with distributed computing frameworks such as Spark
• Ability to design and implement robust model evaluation and validation strategies, including cross-validation and backtesting
• Experience with metrics for time series forecasting accuracy and performance assessment
• Proficiency in cloud-based platforms and tools for deploying and monitoring machine learning models
• Proficiency in building AI agents to automate manual work
• Excellent verbal and written communication skills, capable of conveying complex concepts to a broad audience
• Strong interest and understanding of the housing market and economic factors that influence it
• Passionate about innovative solutions to time series, panel data, and hierarchical forecasting problems
