Senior Applied Scientist, Property Analytics

Responsibilities: — Lead the full lifecycle design, architecture, development, and deployment of advanced automated valuation modeling (AVM) systems for innovative lending products. — Develop and refine machine learning, deep learning, and statistical models for asset valuation, incorporating spatial-temporal trends, macroeconomic factors, market liquidity, and property risk dynamics. — Create robust quantitative frameworks for ongoing model performance evaluation, monitoring, and automated retraining to adapt to real estate market cycles. — Build analytical tools and frameworks to support cross-functional teams in risk, underwriting, and capital markets. — Collaborate closely with data infrastructure and MLOps teams to enhance data capabilities, develop feature stores, and optimize real-time inference infrastructure. — Design data attributes and feature representations using structured, unstructured, and multimodal data sources such as property photos, listing descriptions, and spatial features. — Conduct exploratory data analysis, assess frontier AI capabilities, and provide strategic recommendations to improve decision-making frameworks and valuation accuracy. — Refactor and optimize existing analytical solutions and algorithms to enhance scalability, reliability, and efficiency. Requirements: — Ph.D. or Master's degree in quantitative fields like Computer Science, Data Science, Statistics, Economics, Applied Mathematics, or Machine Learning. — Minimum 5 years industry experience (or 3+ years with Ph.D.) in building and deploying machine learning models in production. — Proven experience developing statistical and machine learning models at account, customer, or real estate asset levels. — Strong understanding and practical application of advanced machine learning techniques, deep learning, spatial analytics, and AI. — Proficient programming skills in Python, SQL, and R at production level. — Demonstrated technical leadership, cross-functional collaboration, and mentoring abilities. — Excellent communication skills to explain complex technical concepts to non-technical stakeholders and executives. Preferred: — Hands-on experience with Automated Valuation Models (AVMs) or automated appraisal systems. — Knowledge of time-to-event/survival analysis, financial econometrics, or spatial-temporal forecasting. — Experience with unstructured data modeling, including computer vision and NLP in real estate. — Familiarity with distributed computing frameworks like Spark and deep learning frameworks such as PyTorch and TensorFlow. — Experience with Monte Carlo simulations, Bayesian inference, or causal machine learning. — Understanding of MLOps best practices, cloud architectures (AWS/GCP), feature stores, and model serving infrastructure. — Progress towards or completion of quantitative/financial certifications like FRM or CFA. Benefits: — Competitive base salary range $175,000 - $225,000 annually plus bonus and benefits. — Full-time remote position. Additional Information: — Identity verification process involving biometric and device validation is required for security. — Official communication only via @kiavi.com or @lendinghome.com email addresses. — No payment requests or communication via WhatsApp or text messages during recruitment. To apply: https://weworkremotely.com/remote-jobs/kiavi-senior-applied-scientist-property-analytics