Data Scientist

• Analyze and validate internal credit, risk, valuation, and recovery models against historical loan and portfolio outcomes
• Build predictive and forecasting models for loan performance, defaults, payoff timing, collateral value changes, recovery outcomes, costs, and timelines
• Design and run backtests, sensitivity analyses, scenario comparisons, and time-based analyses across large historical datasets
• Extract, clean, reconcile, and validate data from large, multi-source lending and real estate datasets
• Identify factors predictive of loan performance, collateral outcomes, and realized recoveries
• Build simple tools allowing stakeholders to explore model results and scenarios
• Investigate and document data quality issues, edge cases, model limitations, and inconsistencies
• Translate analytical findings into recommendations for underwriting, credit, pricing, portfolio management, and risk management
• Maintain clean, reproducible, well-documented analytical and modeling work
• Present analyses and findings clearly to teams and stakeholders
• Participate in daily BLV alignment meetings

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Finance, Economics, or a related field
• 3 to 7 years of experience in data science, applied modeling, or quantitative analytics
• Stable, reliable internet connection
• Professional and dedicated remote working setup
• Background or strong interest in finance, banking, commercial lending, U.S. real estate, or property valuation
• Familiarity with commercial or small business lending is a plus
• Experience with predictive modeling, forecasting, survival/time-to-event analysis, or other time-based estimation problems
• Experience with credit risk, default, loss, recovery, model validation, or scenario/sensitivity analysis is a plus
• Experience reconciling and cleaning data from multiple sources or systems
• Strong proficiency in Python and SQL
• Experience with statistical modeling, machine learning, forecasting, or related quantitative techniques, including model evaluation and validation
• Experience working with large datasets, including writing efficient, performance-conscious data processing code
• Experience building simple, shareable analytical tools or dashboards is a plus
• Strong data communication skills and ability to explain complex analytical and modeling findings to technical and non-technical audiences
• Ability to work autonomously and proactively in ambiguous situations

• Paid time off (PTO)
• Fully remote work environment