Senior Applied Scientist, Property Analytics

Figure; Stati Uniti; Remoto; USD 180.000 - 270.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive compensation

Healthcare benefits

401(k) match

Descrizione del lavoro

Figure Technology Solutions, Inc. is hiring a Senior Applied Scientist, Property Analytics to advance automated valuation models for real estate lending. You will lead ML research, collaborate with cross-functional teams, and drive production deployment on scalable analytics platforms.

You’ll mentor team members, shape technical direction, and partner with MLOps, product, and risk groups to improve valuation accuracy and decisioning frameworks.

Competenze

• PhD or MS in a quantitative discipline with 5+ years of industry ML deployment experience.
• Proven track record building statistical and ML models for production use.

Mansioni

• Lead end-to-end AVM system design, development, and deployment for new lending products.
• Refine models with spatial-temporal data, macro factors, and real estate risk signals.
• Establish robust model monitoring, retraining, and performance evaluation.

Conoscenze

Python

SQL

Machine Learning

Deep Learning

Spatial Analytics

MLOps

Leadership

Communication

Computer Vision

NLP

PyTorch

TensorFlow

Spark

Formazione

PhD/MS in Quant

Strumenti

PyTorch

TensorFlow

Spark

Descrizione del lavoro

About Figure Figure Technology Solutions, Inc. (Nasdaq: FIGR; OPEN: FGRS) is the leading blockchain-native capital marketplace for the origination, funding, sale and trading of tokenized assets. More than 480 partners use its loan origination system and capital marketplace. Collectively, Figure and its partners have originated over $30 billion of loans to date, among other products. The fastest growing components are Figure Connect, its credit marketplace, and Democratized Prime, Figure s on-chain lend-borrow marketplace. Figure s ecosystem also includes DART (Digital Asset Registry Technology) for asset custody and lien perfection, and $YLDS, a yield-bearing stablecoin registered with the SEC as a security under the Securities Act of 1933. Figure is the market leader in real-world asset (RWA) tokenization. The company has received triple-A ratings from S&P and Moody s on multiple loan securitizations, the first of its kind for blockchain finance. We re proud to be recognized as one of Forbes Top 50 Fintech Companies and CNBC s World s Top Fintech Companies.

About the Role

We re looking for a Senior Applied Scientist, Property Analytics to join our growing team! As Senior Applied Scientist, Property Analytics, you ll help us reinvent real estate lending by tackling complex research and machine learning problems related to automated valuation models (AVMs), asset valuation, and process optimization. You ll work alongside engineering, product, and business leaders on a team of 360-degree thinkers who combine advanced analytics and machine learning skills with deep domain knowledge, using some of the most fascinating data sets in the world. You ll lead technical direction, mentor team members, and partner closely with MLOps engineers, Product Managers, Credit Risk Management, and Capital Markets subject matter experts to drive strategic initiatives from initial research to production deployment.

What You’ll Do

• 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 innovative machine learning, deep learning, and statistical models for asset valuation, incorporating spatial-temporal trends, macroeconomic factors, market liquidity, and property risk dynamics
• Establish robust quantitative frameworks for continuous model performance assessment, monitoring, and automated retraining across changing real estate cycles
• Build analytical tools and frameworks to empower cross-functional teams across risk, underwriting, and capital markets
• Partner closely with data infrastructure and MLOps engineering teams to drive initiatives that enhance data capabilities, build feature stores, and streamline real-time inference infrastructure
• Design useful data attributes and feature representations, leveraging structured, unstructured, and multimodal data sources (for example, property photos, listing descriptions, and spatial features)
• Perform exploratory data analysis, evaluate frontier AI capabilities, and make strategic recommendations to improve current decisioning frameworks and valuation accuracy
• Refactor and optimize existing analytical solutions and algorithmic components to improve scalability, reliability, and execution efficiency

What We Look For

• Ph.D. or Master s degree in a quantitative discipline (for example, Computer Science, Data Science, Statistics, Economics, Applied Mathematics, or 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 (preferred)
• Exposure to time-to-event or survival analysis, financial econometric modeling, or spatial-temporal forecasting (preferred)
• Experience handling and modeling unstructured data, including computer vision or natural language processing applications in real estate (preferred)
• Proficiency with distributed computing frameworks (for example, Spark) and modern deep learning frameworks (for example, PyTorch and TensorFlow) (preferred)
• Experience with Monte Carlo simulations, Bayesian inference, or causal machine learning (preferred)
• Familiarity with MLOps best practices, cloud architecture (AWS and GCP), feature stores, and model serv