Applied Scientist, Forecasting
Core
Design and evaluate statistical, econometric, and machine learning methods for forecasting problems to increase forecast accuracy and accelerate delivery timelines.
Role type
Applied Scientist (Forecasting)
Builds
Production forecasting models, backtesting frameworks, and data pipelines for real estate and business decisions.
Domain
Real Estate / Time-series Forecasting / Econometrics
Deliverable
production ML models
Required skills
Time-series forecasting, nowcasting, econometrics, data engineering principles, Python, SQL, model explainability, feature engineering, robust estimation strategies
Preferred skills
Advanced degree in Economics, Statistics, Operations Research, Data Science, Computer Science, Econometrics, Mathematics
Technologies
Python, SQL
Responsibilities
Design and evaluate statistical, econometric, and machine learning methods for forecasting problems; Build backtesting and validation frameworks to assess forecast accuracy, stability, and downstream forecast impact; Own the end‑to‑end modeling lifecycle including scoping, feature engineering, model development, experimentation, deployment, monitoring, and model explainability; Translate forecasts into clear insights and recommendations for senior leadership; Quantify uncertainty and clearly communicate model confidence, limitations, and trade-offs to technical and non-technical audiences; Partner cross‑functionally with finance, product, engineering, marketing, and operations to scale and improve forecasting capabilities; Improve and contribute to shared forecasting tools, data pipelines, and processes used across the company.
Seniority
Mid-Senior (3+ years experience)
