Senior Applied Scientist, Property Analytics

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About This Role

Kiavi is seeking a Senior Applied Scientist to join its Property Analytics team and help transform property lending. In this remote, full-time position, the professional will collaborate with product, engineering, and business groups to tackle challenges in asset valuation, automated valuation models (AVMs), and process optimization. The ideal candidate will guide technical execution, mentor team members, and connect initial research with production deployment alongside MLOps, product management, and risk management…

Job Description

Responsibilities

• Manage the complete architecture, creation, launch, and upkeep of automated valuation modeling systems for new lending offerings
• Design and update deep learning, statistical, and machine learning models for property valuation incorporating spatial-temporal patterns, liquidity, and macro economics
• Set up quantitative standards for ongoing performance tracking, evaluation, and automated retraining through real estate cycles
• Develop analytical frameworks and tools to assist teams in underwriting, risk management, and capital markets
• Collaborate with data infrastructure and MLOps staff to improve data systems, create feature stores, and optimize real-time inference
• Engineer data attributes and features using multimodal, structured, and unstructured sources such as listing details and property photos
• Conduct exploratory analysis and assess frontier artificial intelligence capabilities to enhance valuation accuracy and decision frameworks
• Update and refine existing algorithmic components to boost execution efficiency, scalability, and reliability

Requirements

• 5 or more years of industry experience building and deploying machine learning models in production for applicants with a Master's degree, or 3 or more years for those with a Ph.D.
• Proven history of building statistical and machine learning models focused on customer, account, or real estate asset levels
• Hands-on expertise and understanding of spatial analytics, deep learning, advanced machine learning, and AI capabilities
• Advanced production-level coding proficiency in SQL, Python, and R
• Demonstrated background in cross-functional teamwork, technical leadership, and mentoring junior colleagues
• Written and verbal communication skills capable of breaking down complex mathematical and algorithmic concepts for executive leadership and non-technical partners
• Completion of a secure identity verification process including biometric and device validation

Qualifications

• Ph.D. or Master's degree in a quantitative field such as Statistics, Computer Science, Data Science, Economics, Machine Learning, or Applied Mathematics
• Practical background in building, testing, and releasing Automated Valuation Models or automated appraisal systems
• Familiarity with financial econometric modeling, time-to-event or survival analysis, or spatial-temporal forecasting
• Experience working with and modeling unstructured information, including natural language processing or computer vision in real estate
• Knowledge of modern deep learning tools like PyTorch or TensorFlow, and distributed computing frameworks like Spark
• Experience utilizing causal machine learning, Bayesian inference, or Monte Carlo simulations
• Acquaintance with cloud architectures on GCP or AWS, MLOps standards, model serving infrastructure, and feature stores
• Progress toward or attainment of quantitative or financial credentials such as CFA or FRM

Core Skills

Benefits

• Annual base salary range between $175,000 and $225,000
• Target bonus
• Benefits package

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