- Salmon Group
- Taguig, Southern Manila District
- Full-Time
- 67 days ago
Data Science (Credit Risk).
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Data Science (Credit Risk): our view in 3 lines...
- The Role:This role is for a data scientist focused on credit risk modelling for a fintech banking and lending platform in Southeast Asia.
- The Person:The person will build and improve application and behavioural scorecards, handle the full modelling cycle, prepare documentation for review, and use model outputs to shape approval, pricing, and collections decisions.
- Requirements:The ideal candidate has 3+ years of hands-on experience building credit risk models, with strong Python, pandas, scikit-learn, and SQL, plus experience with model audits and regulatory review.
About the role
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
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Build and improve Application and Behavioral Scorecards that drive underwriting and collections decisions across live credit products
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Work within a regulated environment — prepare model documentation and defend modeling decisions to internal governance and external reviewers
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Collaborate with product, analytics, and engineering to translate model outputs into strategy changes and implementation specs
What you'll own
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Full modeling cycle: data exploration, feature design, model build, validation, deployment specs, and post-production monitoring
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Scorecard quality — tracked through AUC, KS, bad rate, stability index, and assessed against real portfolio outcomes via NPV and backtesting
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Model documentation and governance artifacts ready for regulatory review
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Credit strategy input — your models influence approval, pricing, and collection decisions, not just inform them
What makes you a strong fit
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3+ years of hands-on experience building credit risk models — Application Scorecards and/or Behavioral Scorecards specifically (ECL/IFRS9-only experience does not substitute)
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Participated in model audits, prepared documentation, or supported regulatory review
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Strong Python (pandas, scikit-learn) and SQL — you build models yourself, not just review others' work
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Experience covering the full lifecycle: data, feature engineering, validation, production monitoring
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Experience in the Philippine financial services industry is a strong plus
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Familiarity with Databricks or similar environments is a plus

