Data Scientist

This is a fully remote position, open to applicants in Costa Rica, +5 more countries.

📋 Description

• Conduct analyses and validate internal credit, risk, valuation, and recovery models by comparing them to historical loan and portfolio outcomes.

• Develop predictive and forecasting models to assess loan performance, defaults, payoff timing, changes in collateral value, recovery outcomes, costs, and timelines.

• Create and execute backtests, sensitivity analyses, scenario comparisons, and time-based analyses using extensive historical datasets.

• Extract, cleanse, reconcile, and validate data from large, multi-source lending and real estate datasets.

• Identify predictors of loan performance, collateral outcomes, and realized recoveries.

• Design straightforward tools that enable stakeholders to explore model results and scenarios.

• Investigate and document issues related to data quality, edge cases, model limitations, and inconsistencies.

• Translate analytical insights into actionable recommendations for underwriting, credit, pricing, portfolio management, and risk management.

• Maintain clean, reproducible, and well-documented analytical and modeling processes.

• Clearly present analyses and findings to teams and stakeholders.

• Engage in daily alignment meetings with the BLV team.

⛳️ Requirements

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Finance, Economics, or a related discipline.

• 3 to 7 years of experience in data science, applied modeling, or quantitative analytics.

• A stable and reliable internet connection is essential.

• A professional and dedicated remote working setup is required.

• A background or strong interest in finance, banking, commercial lending, U.S. real estate, or property valuation.

• Familiarity with commercial or small business lending is advantageous.

• Experience with predictive modeling, forecasting, survival/time-to-event analysis, or other time-based estimation methodologies.

• Experience in credit risk, default, loss, recovery, model validation, or scenario/sensitivity analysis is beneficial.

• Proficient in reconciling and cleaning data from multiple sources or systems.

• Strong proficiency in Python and SQL is required.

• Experience with statistical modeling, machine learning, forecasting, or related quantitative techniques, including model evaluation and validation.

• Proven experience working with large datasets, including writing efficient and performance-conscious data processing code.

• Experience in building simple, shareable analytical tools or dashboards is a plus.

• Excellent data communication skills with the ability to articulate complex analytical and modeling findings to both technical and non-technical audiences.

• Capability to work autonomously and proactively in ambiguous situations.

🏝️ Benefits

• Paid time off (PTO).

• Fully remote work environment.