Data Science (Credit Risk)

Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines.

7M+ app downloads. 2M+ monthly active users. 7000+ partner stores. US$350M+ raised from top-tier investors.

Here, you get real ownership from day one, high standards, and direct communication. You'll work alongside a team that combines deep local knowledge and experience scaling regulated financial companies from scratch.

If you want to solve complex problems at scale and directly impact how millions of people access and manage money, come build with us.

Southeast Asia's fintech moment starts here.

About the role

You'll own credit scoring across the full customer lifecycle — from application decisions to behavioral models that drive portfolio profitability

What you'll do

• Build and improve Application and Behavioral Scorecards that drive underwriting and collections decisions across live credit products

• Work within a regulated environment — prepare model documentation, and defend modeling decisions to internal governance and external reviewers

• Collaborate with product, analytics, and engineering to translate model outputs into strategy changes and implementation specs

What you'll own

• Full modeling cycle: data exploration, feature design, model build, validation, deployment specs, and post-production monitoring

• Scorecard quality — tracked through AUC, KS, bad rate, stability index, and assessed against real portfolio outcomes via NPV and backtesting

• Model documentation and governance artifacts ready for regulatory review

• Credit strategy input — your models influence approval, pricing, and collection decisions, not just inform them

What makes you a strong fit

• 3+ years of hands-on experience building credit risk models — Application Scorecards and/or Behavioral Scorecards specifically (ECL/IFRS9-only experience does not substitute)

• Participated in model audits, prepared documentation, or supported regulatory review

• Strong Python (pandas, scikit-learn) and SQL — you build models yourself, not just review others' work

• Experience covering the full lifecycle: data, feature engineering, validation, production monitoring

• Familiarity with Databricks or similar environments is a plus