Senior Data Scientist – Risk Modeling (Senior Data Scientist – Modelado de Riesgos) - Hybrid

What you'll do

• Build and manage the full lifecycle of credit risk models, from problem definition and feature engineering through validation, monitoring, and recalibration.
• Analyze portfolio data and performance using SQL and Python to improve underwriting, credit limits, segmentation, and risk policies.
• Partner with Risk, Data, Engineering, Finance, and Operations to implement models and communicate actionable findings.

What they ask for

• 4–6+ years of experience in Data Science, Risk Analytics, Credit Risk, or related analytical roles, including at least 2 years developing or validating credit risk or other predictive risk models.
• Proficiency in Python and SQL; experience with Databricks or a similar cloud-based analytics platform and predictive modeling tools such as scikit-learn, LightGBM/XGBoost, or PyTorch.
• Understanding of the full model lifecycle and credit risk analytics, plus experience with large financial or transactional datasets and data quality.
• Working proficiency in English and Spanish; academic background in a related quantitative field.

Nice to have

• Experience in fintech, lending, credit cards, payments, or B2B financial products, or with Latin American credit markets.
• Experience with credit bureau or alternative data, risk methodologies such as PD/ECL/LGD/EAD, reject inference, MLflow, GitHub, or model implementation.

Tools

SQL, Python, Databricks, MLflow, GitHub, scikit-learn, LightGBM/XGBoost, PyTorch