About the roleAs 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, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $139,400.00 - $222,600.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia the standard base pay range for this role is $132,400.00 - $211,600.00 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.
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.
- 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 agent to automate manual work
- 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
