Data Scientist
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Job Description
Data Scientist modeling loan performance, credit risk, recoveries, and real estate collateral for WBL’s U.S. commercial lending business. Building predictive models and validating portfolio outcomes.
Responsibilities:
- 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
Requirements:
- 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
Benefits:
- Paid time off (PTO)
- Fully remote work environment
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