About the teamJoin our dynamic team, leveraging a comprehensive database of over 100 million homes, transactions, and users to predict and analyze housing market trends. As an Applied Scientist specializing in traditional econometrics and machine learning with a focus on time series and hierarchical forecasting, you will play a critical role in shaping our understanding and forecasting of the housing market.

About the role

As an Applied Scientist you will actively participate in 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 team.

Scenario Modeling: Develop scenario models that capture a wide range of housing market outcomes and contribute to a company-wide stress-testing framework.

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, Colorado, Connecticut, Hawaii, Nevada, New York, Washington state, and Washington DC the standard base pay range for this role is $132,400.00 - $211,600.00 Annually. This base pay range is specific to California, Colorado, Connecticut, Hawaii, Nevada, New York, Washington state, and Washington DC 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.

Who you are

Keen Housing Market Observer: Strong interest and understanding of the housing market and the economic factors that influence it.

Innovative Problem Solver: Passionate about working on innovative solutions to time series, panel data, and hierarchical forecasting problems, conducting independent research, and applying research methods to real-world problems.

Technical Proficiencies:

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 software development best practices and version control systems (e.g., Git).

Proficient in cloud-based platforms and tools for deploying and monitoring machine learning models.

Communication Skills: Excellent verbal and written communication skills, capable of conveying complex concepts to a broad audience.

Experience: 2+ years of proven experience working in time series/spatial forecasting space.

Educational Background: Master’s or PhD degree in Mathematics, Statistics, Economics, Econometrics, Physics, Earth Sciences, or a related scientific field 

Get to know us

Zillow is reimagining real estate to make home a reality for more and more people.

As the most-visited real estate website in the United States, Zillow® and its affiliates help movers find and win their home through digital solutions, first class partners, and easier buying, selling, financing and renting experiences. Millions of people visit Zillow Group sites every month to start their home search, and now they can rely on Zillow to help make it easier to move. The work we do helps people get home and no matter what job you're in, you will play a critical role in making home a reality for more and more people.

Our efforts to streamline the real estate transaction are supported by a deep-rooted culture of innovation, our passion to redefine the employee experience, a fundamental commitment to Equity and Belonging, and world-class benefits. These benefits include comprehensive medical, dental, vision, life, and disability coverages as well as parental leave, family benefits, retirement contributions, and paid time off. We’re also setting the standard for work experiences of the future, where our employees are supported in doing their best work and living a flexible, well-balanced life. But don’t just take our word for it. Read recent reviews on Glassdoor and recent recognition from multiple organizations, including: the 100 Best Companies to Work For, Glassdoor Employees’ Choice Award, Bloomberg Gender-Equality Index, Human Rights Campaign (HRC) Corporate Equity Index, and TIME 100 Most Influential Companies list.

Zillow Group is an equal opportunity employer committed to fostering an inclusive, innovative environment with the best employees. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please contact your recruiter directly.

Applicants who receive job offers from Zillow Group will be asked to sign a Proprietary Rights Agreement which includes confidentiality, intellectual property assignment, customer and employee non-solicitation, and non-competition provisions. If you are contacted for a role at Zillow Group and wish to review a copy of the Proprietary Rights Agreement prior to receiving an offer, you may request a copy from your Recruiter.