Senior Applied Scientist, Property Analytics

Role overview Lead the research and production delivery of automated property valuation models for lending products. The role combines advanced statistical and machine learning work with technical direction, mentoring, and close partnership with product, risk, data, and capital markets teams. Models must account for changing property markets and support reliable decision-making at scale.

Responsibilities - Design, build, and deploy automated valuation systems from research through production. - Develop statistical, machine learning, and deep learning models incorporating geographic and time-based patterns, economic factors, liquidity, and property risk. - Establish model evaluation, monitoring, and retraining approaches that respond to changing market conditions. - Create analytical tools for risk, underwriting, and capital markets partners. - Work with data and MLOps teams on data capabilities, feature stores, and real-time inference. - Develop features from structured and unstructured sources, evaluate emerging AI approaches, and improve existing solutions for scale and reliability.

Requirements - A master’s or doctoral degree in a quantitative discipline, plus substantial industry experience building and deploying production ML models; the stated experience threshold is five years, or three years with a Ph.D. - Demonstrated success building statistical or machine learning models for customers, accounts, or real estate assets. - Applied knowledge of advanced ML, deep learning, spatial analytics, and AI. - Production programming experience in Python, SQL, and R. - Technical leadership, mentoring, and cross-functional collaboration skills. - Ability to explain complex quantitative ideas clearly to technical and non-technical audiences.

Nice to have - Automated valuation or appraisal systems; survival analysis, financial econometrics, or spatial-temporal forecasting. - Computer vision or language processing for unstructured data, distributed computing, and deep learning frameworks. - Bayesian methods, Monte Carlo simulation, causal ML, MLOps, cloud systems, feature stores, or model serving. - Relevant quantitative or financial certifications.

Benefits and work setup The listed U.S. compensation is $132,000–$198,000 base, a 25% annual bonus target paid quarterly, and equity. Benefits include health coverage, savings and commuter accounts, retirement benefits, life and disability insurance, paid time off, up to 12 weeks of paid family leave, and continuing education reimbursement. Visa sponsorship is not offered.