Applied Scientist

Working remotely in a full-time capacity, the Applied Scientist will develop hierarchical forecasts for the housing market utilizing econometric methods and machine learning, while collaborating with cross-functional teams to deploy models and communicate insights to stakeholders. Key responsibilities Collaborate with applied scientists, data scientists, and economists to create hierarchical forecasts for various regional housing markets Identify economic and demographic factors influencing housing market trends and extract relevant features for predictive modeling Contribute to monthly forecast production and develop scenario models for stress-testing frameworks Required qualifications Master's or PhD degree in Mathematics, Statistics, Economics, Econometrics, Physics, Earth Sciences, or a related scientific field 2+ years of experience in time series or spatial forecasting 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 cloud-based platforms for deploying and monitoring machine learning models