Data Scientist
W
World Business LendersFinancial Services
San José, San José Province, Costa Rica. San Salvador, San Salvador Department, El Salvador. Brasília, Brasília, Brazil. Buenos Aires, Buenos Aires, Argentina. Guatemala City, Guatemala, Guatemala. Managua, Managua, Nicaragua, 9:00 AM – 6:00 PM Eastern Standard TimeContractMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 3 to 7 years
- Required Skills
- PythonSQLMachine Learning
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
- Strong proficiency in Python and SQL
- Experience with statistical modeling, machine learning, forecasting, or related quantitative techniques
- Experience working with large datasets, including writing efficient, performance-conscious data processing code
- Experience reconciling and cleaning data from multiple sources or systems
- Ability to build simple, shareable analytical tools or dashboards
- Stable, reliable internet connection
- Professional and dedicated remote working setup
- Strong data communication skills
Responsibilities
- Analyze and validate internal credit, risk, valuation, and recovery models against real, historical loan and portfolio outcomes
- Build predictive and forecasting models that estimate future loan performance, default or 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
- Work with large, multi-source lending and real estate datasets to extract, clean, reconcile, and validate data for analysis and modeling
- Identify the factors most predictive of loan performance, collateral outcomes, and realized recoveries
- Investigate and document data quality issues, edge cases, model limitations, and inconsistencies found in large datasets
- Translate analytical and modeling findings into clear, actionable recommendations for underwriting, credit, pricing, portfolio management, and risk management
- Maintain clean, reproducible, well-documented analytical and modeling work
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