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Data Scientist

El Salvador, Guatemala · remote · Salary not listed

Role overview

About World Business Lenders

At World Business Lenders (WBL), we provide flexible, short-term commercial loans backed by real estate to help small and medium-sized businesses across the United States — particularly those facing difficulties with traditional financing. We're a fast-moving, results-driven organization that takes security seriously as we continue to grow.
This is a Full-Time Independent Contractor role with working hours from 9:00 AM – 6:00 PM Eastern Standard Time, Monday through Friday. We request that all CVs be submitted in English.

This role sits within WBL's Data team and focuses on credit, loan performance, collateral, and recovery modeling, helping the business better understand and predict how loans and their underlying real estate collateral behave over their life cycle.
The work involves large, national real estate and lending datasets. This is real data at scale work, often involving millions of records and large, imperfect, real world files.
A growing part of this role is building predictive and forecasting models using historical, realized outcomes to estimate future loan performance, default and payoff behavior, collateral value changes, recovery timing, costs, and expected outcomes. This includes applying machine learning and statistical techniques where appropriate, not just descriptive or backward-looking analysis.
This role will contribute to the development and validation of WBL's internal loan valuation and risk models and comes with a high degree of ownership and autonomy as our data and modeling capabilities expand. .

Early on, the day starts by joining the daily BLV alignment meeting to stay current on the project's philosophy, direction, and where things currently stand.
Most of the day is spent hands-on with data: pulling and cleaning records from large national real estate and lending datasets, cross-checking fields across multiple sources, and investigating data quality issues as they come up. A meaningful part of this involves directing AI-assisted tools to run deep, multi-step analyses efficiently, while carefully validating their output rather than accepting it at face value.
Several days a week involve model-focused work: running back tests of internal valuation and risk models against real, historical outcomes, building or refining predictive/forecasting components, and running sensitivity analyses.