Contract Based Data Scientist (BS Statistics/Mathematics Grad or 4th yr Student)

TRBank, Inc. (A Rural Bank); Metro Manila; On-site; PHP 900,000 - 1,800,000; Full time

Benefits offered by this job

Job summary

TRBank, Inc. (A Rural Bank) seeks a data analytics professional to develop and maintain statistical and machine-learning models for credit scoring, risk segmentation, and delinquency forecasting.

You will translate complex data into clear, decision-ready insights for Management, Credit, Risk, and Collections, build dashboards, and collaborate with the Data Engineer to ensure data quality and production-ready model pipelines while adhering to regulatory expectations.

Responsibilities

• Analytics & Modeling: develop, test, and maintain statistical and machine-learning models for credit scoring and risk segmentation, early warning signals for delinquency and default, collections prioritization and recovery strategies, and portfolio performance and yield optimization.
• Business Insights & Decision Support: translate complex data into clear, decision-ready insights for Management, Credit, Risk, Collections, and Business teams; build dashboards, reports, and analytical outputs to support loan approvals, pricing, portfolio monitoring, and branch/product/channel performance.
• Data Collaboration: collaborate with the Data Engineer to define data requirements and feature sets, ensure data quality, consistency and usability, and operationalize models into production pipelines; validate datasets from core banking, LOS, collections systems, and external sources.
• Model Governance & Compliance: ensure models are documented, explainable, auditable, aligned with BSP expectations; support internal audits, risk reviews, and regulatory examinations; participate in model reviews, recalibration, and performance monitoring.
• Continuous Improvement: identify opportunities to improve credit decisioning speed and quality, reduce NPLs and credit losses, and increase recovery rates and operational efficiency; stay current with advances in data science relevant to banking and lending.

Skills

Analytics & Modeling

Data Collaboration

Model Governance

Continuous Improvement

Job description

SPECIFIC DUTIES AND RESPONSIBILITIES:

1. 
   Analytics & Modeling Develop, test, and maintain statistical and machine-learning models for:
   
   
   
   • Credit scoring and risk segmentation
   
   
   • Early warning signals (EWS) for delinquency and default
   
   
   • Collections prioritization and recovery strategies
   
   
   • Portfolio performance and yield optimization
   
   
   
   Apply models pragmatically, balancing accuracy, explainability, and regulatory acceptability.
   


2. 
   Business Insights & Decision Support Translate complex data into clear, decision-ready insights for Management, Credit, Risk, Collections, and Business teams.
   Build dashboards, reports, and analytical outputs that support:
   
   
   
   • Loan approvals and pricing
   
   
   • Portfolio monitoring (NPLs, roll rates, vintage analysis)
   
   
   • Branch, product, and channel performance
   
   
   
   Answer business questions directly.
   


3. 
   Data Collaboration Work closely with the Data Engineer to:
   
   
   
   • Define data requirements and feature sets
   
   
   • Ensure data quality, consistency, and usability
   
   
   • Operationalize models into production pipelines
   
   
   
   Validate datasets extracted from core banking, LOS, collections systems, and external data sources.
   


4. 
   Model Governance & Compliance Ensure models are documented, explainable, and auditable, aligned with BSP expectations.
   Support internal audit, risk reviews, and regulatory examinations involving analytics or models.
   Participate in model reviews, recalibration, and performance monitoring.
   


5. 
   Continuous Improvement Identify opportunities to:
   
   
   
   • Improve credit decisioning speed and quality
   
   
   • Reduce NPLs and credit losses
   
   
   • Increase recovery rates and operational efficiency
   
   
   
   Stay current with practical advances in data science relevant to banking and lending.