Data Scientist, Credit Risk

SALMON ROBOTICS LIMITED

Data Scientist managing credit scoring across customer lifecycle for a fintech company in Southeast Asia. Building models to drive portfolio profitability and influence risk strategy in a regulated environment.

Posted 7/29/2026full-timeTaguig • 🇵🇭 PhilippinesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and validating credit risk models, particularly Application and Behavioral Scorecards, while ensuring compliance with regulatory standards. Proficient in translating model outputs into actionable strategies and collaborating across teams to enhance credit decision-making processes.

Highest-signal resume keywords

Credit Risk ModelingPython ProgrammingSQL ProficiencyModel DocumentationData Exploration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Credit Risk ModelsApplication ScorecardsBehavioral ScorecardsFeature EngineeringModel ValidationProduction Monitoring

Tools & Technologies

DatabricksPandasScikit-learn

Industry Keywords

Regulated EnvironmentModel AuditsGovernance ArtifactsAUCKSBad RateStability IndexNPVBacktesting

Tech Stack

Tools & technologies

PandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

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, support BSP audits, 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
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

Requirements

What you’ll need

3+ years of hands-on experience building credit risk models (Application Scorecards and/or Behavioral Scorecards specifically)
Direct BSP exposure: participated in model audits, prepared documentation, or supported regulatory review
Strong Python (pandas, scikit-learn) and SQL
Experience covering the full lifecycle: data, feature engineering, validation, production monitoring
Familiarity with Databricks or similar environments is a plus

Benefits

Comp & perks

Ownership from day one
High standards
Direct communication