Senior Applied Scientist, Property Analytics

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🔥 2 minutes ago

🇺🇸 United States – Remote

💵 $132k - $198k / year

⏰ Full Time

🟠 Senior

🧬 Research Scientist

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Figure

WebsiteLinkedInAll Job Openings

501 - 1000 employees

💸 Finance

💳 Fintech

🏦 Banking

Finance • Fintech • Banking

Figure is transforming the trillion-dollar financial services industry using blockchain technology. Founded in 2018 by technology entrepreneur Mike Cagney, Figure has established itself as a groundbreaking player in the fintech space with a focus on home equity products, consumer lending, and asset management. Leveraging the Provenance Blockchain, Figure delivers innovative solutions for loan origination, equity management, and financial transactions, aiming to provide speed, efficiency, and savings for both consumers and institutions. It stands out as one of the few unicorns in the financial technology sector.

📋 Description

• 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, including 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 • Lead technical direction, mentor team members, and partner with MLOps engineers, Product Managers, Credit Risk Management, and Capital Markets subject matter experts to drive initiatives from initial research to production deployment

🎯 Requirements

• 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 serving infrastructure (preferred) • Progress towards or completion of relevant quantitative or financial certifications (for example, FRM or CFA) (preferred) • Adhere to all company security policies and data handling procedures • Complete mandatory security awareness training within required timeframes • Promptly report any suspected security incidents or suspicious activity to the Security team • Must be authorized to work in the United States; Figure will not sponsor work visas

🏖️ Benefits

• Comprehensive medical, dental, and vision coverage, with 100% employer-paid premiums for employees and their dependents on select plans • Company HSA, FSA, Dependent Care FSA, 401(k), and commuter benefits • Employer-paid life and disability insurance • 11 observed holidays and PTO plan • Up to 12 weeks of paid family leave • Continuing education reimbursement • 25% annual bonus target, paid quarterly • Company equity in the form of RSUs

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