Applied Scientist

Overview

Applied Scientist on Zillow's housing market forecasting team, developing hierarchical time series forecasts, extracting predictive economic and demographic features, and deploying models into production.

What you'll do

• Work alongside applied scientists, data scientists and economists to develop hierarchical forecasts for the housing market at various regional levels.
• 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.
• Collaborate closely with the engineering team to deploy models into production environments.
• Contribute to monthly forecast production, communicating forecast performance and the housing market outlook to business partners and senior leadership.
• Develop scenario models that capture a wide range of housing market outcomes and contribute to a company-wide stress-testing framework.

What you'll need

• Strong interest in and understanding of the housing market and the economic factors that influence it.
• Passion for innovative solutions to time series, panel data, and hierarchical forecasting problems, including independent research and applying research methods to real-world problems.
• Strong foundation in traditional econometric methods and modern machine learning techniques.
• Proficient 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.
• Proficient in cloud-based platforms and tools for deploying and monitoring machine learning models.
• Proficient in building AI agents to automate manual work.
• Excellent verbal and written communication skills, capable of conveying complex concepts to a broad audience.
• 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.

Details

• Remote position; work from a physical location of your choice that must be identified to the Company.
• U.S. employees may live in any of the 50 United States, with limited exceptions.
• Eligible for equity awards based on experience, performance and location.
• Base pay range of $139,400.00 - $222,600.00 annually in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC; $132,400.00 - $211,600.00 annually in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia.