Credit Risk Modelling Specialist (Fintech)

MHA Consultancy Services Sdn Bhd; Kuala Lumpur; Ibrido; MYR 120.000 - 180.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work model

Medical coverage

Performance bonus

Descrizione del lavoro

MHA Consultancy Services Sdn Bhd in Kuala Lumpur is seeking a Credit Risk Modelling Specialist to design, validate, and optimize predictive credit risk models for our loan portfolio. You will apply statistical methods, machine learning, and behavioral analytics to assess repayment capacity and drive profitability.

The role emphasizes end-to-end model development, monitoring, governance, and cross-functional collaboration with Risk, Product, and Data Engineering teams in a hybrid work environment.

Competenze

• Bachelor’s degree or higher in a quantitative field.
• Strong Python or R for statistical modeling and ML development.
• Advanced SQL skills to query large datasets.
• Solid understanding of credit risk concepts (PD, Credit Scoring, Repayment Behavior).
• Experience in scorecard development, feature engineering, backtesting, and performance monitoring.
• FinTech/Consumer Finance/Digital Lending experience preferred.

Mansioni

• Design and build predictive credit risk models (PD, scorecards) for loan portfolios.
• Develop driver-based models to optimize credit limits and loan amounts.
• Perform data analytics and feature engineering on large historical datasets.
• Monitor model performance and recalibrate to maintain predictive power.
• Collaborate with Risk, Product, and Data Engineering teams to align deployments.

Conoscenze

Python

R

SQL

Credit risk

Statistical modeling

Feature engineering

Backtesting

Model monitoring

Formazione

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Actuarial Science, Finance or related quantitative field

Strumenti

SQL

Descrizione del lavoro

Impactful Role: Drive end-to-end credit risk decisions using cutting-edge predictive modeling and ML algorithms.

Data-Driven Culture: Work with large-scale, high-frequency transactional and loan repayment data.

Growth & Innovation: Opportunity to shape lending strategies, approval limits, and portfolio growth.

Competitive Package: Attractive salary, performance bonuses, hybrid working arrangements, and comprehensive medical coverage.

Location: MRT Taman Mutiara (direct accessible by public transport)
Work Mode: Hybrid

Job Summary

We are seeking an analytical and forward-thinking Credit Risk Modelling Specialist to design, validate, and optimize predictive credit risk models for our loan portfolio. In this role, you will leverage statistical methodologies, machine learning, and behavioral analytics to assess customer repayment capacity, optimize credit limits, and drive profitability.

Key Responsibilities

Predictive Model Development: Design and build robust scorecard/credit risk models (PD, Scorecards) using statistical methods and Python/R to evaluate loan applicant repayment capability and creditworthiness.

Credit Limit & Approval Optimization: Develop driver-based models to recommend dynamic credit limits and optimal loan amounts based on customer risk profiles.

Data Analytics & Feature Engineering: Analyze large datasets of historical loan performance, repayment behaviors, and alternative data to engineer predictive variables.

Model Monitoring & Recalibration: Regularly track model accuracy, stability (PSI/CSI), and Gini/KS performance against actual repayment outcomes; recalibrate algorithms to maintain high predictive power.

Commercial Risk Strategy: Translate complex model outputs into actionable strategies for credit underwriting, approval rates, pricing, and portfolio risk management.

Validation & Governance: Conduct scenario analysis, backtesting, and maintain clear technical model documentation to ensure model transparency and reliability.

Cross-Functional Collaboration: Partner with Risk, Product, and Data Engineering teams to align model deployments with business growth targets and risk appetite.

Requirements

Education: Bachelor’s Degree or higher in Data Science, Statistics, Mathematics, Computer Science, Actuarial Science, Finance, or a related quantitative discipline.

Technical Stack:

Python or R for statistical modeling and machine learning.

Advanced SQL skills to query and manipulate large transactional/loan datasets.

Domain Knowledge: Solid understanding of credit risk concepts (Probability of Default, Credit Scoring, Repayment Behavior, Limit Assessment).

Experience: Hands-on experience in scorecard development, feature engineering, model optimization, backtesting, and performance monitoring. (Experience in FinTech, Consumer Finance, Digital Lending, or Retail Banking is highly preferred)

Soft Skills: Strong commercial mindset with the ability to articulate technical insights clearly to non-technical business leaders.