Credit Scoring Data Scientist

📋 Description

• Develop credit scoring models (both application and behavioral) from the ground up.

• Manage the complete modeling lifecycle: data exploration, feature engineering, model development, validation, deployment, and monitoring.

• Assess models using AUC, KS, Gini, PSI, and bad rate metrics.

• Supervise model performance in production and initiate recalibration or retraining as necessary.

• Analyze model impact through NPV, backtesting, and actual portfolio performance.

• Convert models into implementation-ready specifications for decision engines.

• Collaborate closely with product and risk teams to refine approval strategies, cut-offs, and pricing.

• Contribute to the evolution of credit policy and risk strategy beyond just model development.

• Take complete ownership of your models, from raw data to their business impact.

• Maintain a strong emphasis on real-world performance rather than solely offline metrics.

⛳️ Requirements

• A minimum of 2 years of practical experience specifically in credit risk modeling (as opposed to generic data science).

• Demonstrated expertise in building or validating credit scoring models:

• Application and/or behavioral scoring.

• Strong grasp of:

• PD modeling.

• AUC, KS, and Gini metrics.

• Stability metrics such as PSI and CSI.

• Practical experience with the entire model lifecycle in production.

• Capability to construct models from scratch rather than just maintaining existing ones.

• Proficient in Python (pandas, scikit-learn) and SQL.

• Experience with lending or credit products (such as loans, credit cards, BNPL, etc.).

• Familiarity with translating models into production and decision engine logic.

• Understanding of business impact assessment (including NPV, backtesting, and portfolio metrics).

• Experience utilizing real lending data (e.g., bureau, transactional, credit history).

• Ability to collaborate cross-functionally with product, engineering, and risk teams.

🏝️ Benefits

• A willingness to relocate to the Manila headquarters is a significant advantage.