Senior Applied Scientist, Property Analytics

Kiavi

. Lead the end-to-end design, architecture, development, and deployment of state-of-the-art automated valuation modeling (AVM) systems for novel lending products .

Posted 11/5/2026full-timeRemote • United StatesSenior💰 $175,000 - $225,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying Automated Valuation Models (AVMs) and advanced machine learning techniques, with a strong focus on statistical modeling, data analysis, and cross-functional collaboration. Proven ability to lead technical teams, mentor junior members, and communicate complex concepts effectively to diverse stakeholders.

Highest-signal resume keywords

Automated Valuation Models (AVMs)Machine Learning Model DevelopmentPython ProgrammingStatistical ModelingTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningStatistical AnalysisExploratory Data AnalysisData ModelingFeature EngineeringTime-to-Event AnalysisNatural Language ProcessingComputer VisionBayesian Inference

Soft Skills

Cross-Functional CollaborationMentoringCommunication Skills

Tools & Technologies

SQLRSparkPyTorchTensorFlowAWSGCPMLOps Best Practices

Certifications & Qualifications

Ph.D. or Master's DegreeFRMCFA

Industry Keywords

Real EstateFinancial EconometricsSpatial-Temporal ForecastingRisk ManagementCapital Markets

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPythonPyTorchSparkSQLTensorflow

About the role

Key responsibilities & impact

Lead the end-to-end design, architecture, development, and deployment of state-of-the-art automated valuation modeling (AVM) systems for novel lending products
Architect and refine machine learning, deep learning, and statistical models for asset valuation using spatial-temporal trends, macroeconomic factors, market liquidity, and property risk dynamics
Establish quantitative frameworks for continuous model performance assessment, monitoring, and automated retraining across changing real estate cycles
Build analytical tools and frameworks for cross-functional teams across risk, underwriting, and capital markets
Partner with data infrastructure and MLOps engineering teams to enhance data capabilities, build feature stores, and streamline real-time inference infrastructure
Design data attributes and feature representations using structured, unstructured, and multimodal data sources
Perform exploratory data analysis, evaluate frontier AI capabilities, and recommend improvements to decisioning frameworks and valuation accuracy
Refactor and optimize analytical solutions and algorithmic components for scalability, reliability, and execution efficiency
Lead technical direction, mentor team members, and partner with engineering, product, business, Credit Risk Management, and Capital Markets stakeholders

Requirements

What you’ll need

Ph.D. or Master's degree in a quantitative discipline (e.g., Computer Science, Data Science, Statistics, Economics, Applied Mathematics, Machine Learning) with 5+ years of industry experience (or 3+ years with a Ph.D.) building and deploying machine learning models in production
Proven track record of developing statistical and machine learning models at an account, customer, or real estate asset level
Solid understanding and hands-on application of advanced machine learning techniques, deep learning, spatial analytics, and AI capabilities
Strong production-level programming skills in Python, SQL, and R
Demonstrated experience in technical leadership, cross-functional collaboration, and mentoring junior team members
Excellent oral and written communication skills with the ability to explain complex technical, algorithmic, and mathematical concepts clearly to non-technical stakeholders and executive leadership
Hands-on experience developing, evaluating, and deploying Automated Valuation Models (AVMs) or automated appraisal systems
Exposure to time-to-event/survival analysis, financial econometric modeling, or spatial-temporal forecasting
Experience handling and modeling unstructured data, including computer vision or natural language processing applications in real estate
Proficiency with distributed computing frameworks (e.g., Spark) and modern deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience with Monte Carlo simulations, Bayesian inference, or causal machine learning
Familiarity with MLOps best practices, cloud architecture (AWS/GCP), feature stores, and model serving infrastructure
Progress towards or completion of relevant quantitative/financial certifications (e.g., FRM or CFA)

Benefits

Comp & perks

Bonus
Benefits
Secure identity verification process using biometric and device validation
Equal Opportunity Employer
E-Verify used to confirm identity and employment eligibility of new hires