Data Scientist (Credit Scoring)

As a Data Scientist in our Credit Risk team, you’ll work on improving both application scoring (to enhance onboarding decisions) and behavioral scoring (to increase portfolio profitability). You’ll be responsible for the full modeling cycle: from exploring data and identifying meaningful patterns, to building and validating models, assessing their business impact, writing clear implementation requirements, and monitoring production performance.

The role goes beyond modeling - you’ll collaborate with product managers, analysts, and engineers to understand business context, generate and test hypotheses, and continuously refine our decision-making strategy. We operate in a modern, data-driven environment where models and statistics drive key decisions, and the infrastructure supports fast iteration and deployment.

Each task is evaluated through the lens of business value - there’s no such thing as work “for the drawer.” This is a high-responsibility, high-impact role for someone ready to influence strategy, own results, and gain deep exposure to credit data, user behavior, and market dynamics.

Responsibilities:

• Build credit scoring models (application & behavioral) from scratch

• Own the full modeling lifecycle: data exploration → feature engineering → model development → validation → deployment → monitoring

• Validate models using AUC, KS, Gini, PSI, and bad rate

• Monitor model performance in production and initiate recalibration or retraining when needed

• Evaluate model impact using NPV, backtesting, and real portfolio performance

• Translate models into implementation-ready specs for decision engines

• Work closely with product and risk to adjust approval strategies, cut-offs, and pricing

• Contribute to credit policy and risk strategy evolution, not just model development

• Take full ownership of your models - from raw data to business impact

• Operate with a strong focus on real-world performance, not offline metrics
  

What we expect from candidate:

• 2+ years of hands-on experience specifically in credit risk modeling (not generic data science)

• Proven experience building or validating credit scoring models:

  • Application and/or behavioral scoring

• Strong understanding of:

  • PD modeling

  • AUC / KS / Gini

  • Stability metrics (PSI, CSI)

• Hands-on experience with the full model lifecycle in production

• Ability to build models from scratch, not only maintain existing ones

• Strong Python (pandas, scikit-learn) and SQL skills

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

• Experience translating models into production / decision engine logic

• Understanding of business impact evaluation (NPV, backtesting, portfolio metrics)

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

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

• Willingness to relocate to Manila HQ is a strong advantage