Applied Scientist

Zillow

21h • $129,500 - $217,700 • Remote

About The Position

Our team uses Zillow’s rich data on more than 100 million homes, transactions, and users to understand and forecast housing market trends across the country. We combine econometrics and modern machine learning to deliver insights that guide Zillow’s strategy, help our partners make informed decisions, and improve how customers navigate buying, selling, and financing their homes. About the role As an Applied Scientist, you will develop and refine forecasting models that power Zillow’s view of the housing market at national, regional, and local levels. You will collaborate closely with other applied scientists, data scientists, economists, and engineers to turn large-scale housing and economic data into actionable insights for senior leaders and partners across the company. This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions. In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $136,300.00 - $217,700.00 annually. This base pay range is specific to these locations and may not be applicable to other locations. In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $129,500.00 - $206,900.00 annually. The base pay range is specific to these locations and may not be applicable to other locations. In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.

Requirements

• Demonstrated interest in leveraging AI tools (e.g., LLMs, copilots, automation frameworks) to improve research productivity and forecasting workflows.
• Passion for experimenting with emerging technologies and incorporating them into day-to-day analytical work to increase efficiency and impact.
• Strong interest in the housing market and the economic, demographic, and policy factors that influence housing outcomes along with the ability to explain complex quantitative concepts and tradeoffs to diverse audiences.
• 2+ years of experience working with time series and/or spatial forecasting problems, ideally including hierarchical or panel data settings in an academic or industry environment.
• Solid foundation in traditional econometric methods and modern machine learning techniques relevant to forecasting.
• Hands-on experience preparing and processing large-scale time series or panel datasets, including data cleaning, preprocessing, and feature engineering; familiarity with distributed computing frameworks (such as Spark) is a plus.
• Ability to design and apply appropriate model evaluation and validation approaches for time series forecasting, including backtesting, cross-validation strategies, and forecasting accuracy metrics.
• Proficiency with software development best practices and tools (for example, version control systems such as Git and cloud-based platforms for deploying and monitoring models).
• Master’s or PhD degree in Mathematics, Statistics, Economics, Econometrics, Physics, Earth Sciences, or a related quantitative field, or equivalent practical experience.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level