Manager, Data Analytics & Modelling

CTOS; Selangor; On-site; MYR 100,000 - 130,000; Full time

Job summary

A leading credit bureau in Malaysia is seeking an experienced Analytics Modelling & Analytics Manager to lead the development of advanced risk models. This role focuses on building and validating statistical and machine learning models for decision-making across the credit lifecycle. The ideal candidate will have 5-7 years of hands-on experience, strong technical modelling expertise in Python, SAS, SQL, and excellent stakeholder management skills. Join a collaborative environment with strong career growth potential.

Benefits

Opportunity to shape bureau data models

Exposure to high-impact projects

Strong career growth potential

Qualifications

• 5–7 years’ hands-on experience in credit risk model development.
• Solid understanding of credit bureau and alternative data.
• Experience in model governance, validation, and regulatory compliance.

Responsibilities

• Design, develop, validate, and maintain predictive risk models.
• Build specialized models including bureau scorecards and collections models.
• Ensure models comply with regulatory standards and internal governance.

Skills

Statistical modelling techniques

Python

SAS

SQL

Big data management tools

Communication skills

Stakeholder engagement

Education

Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Data Science, or related field

Tools

AWS

XGBoost

LightGBM

Neural networks

Job description

We are seeking an experienced Analytics Modelling & Analytics Manager to lead the development of advanced risk models for a leading credit bureau. The role focuses on building and validating statistical and machine learning models that power decision-making across the credit lifecycle.

The scope includes both traditional bureau models and alternative data models, depending on use cases. The successful candidate will combine strong technical modelling expertise with hands‑on data processing skills, managing projects end‑to‑end across diverse data sources.

Key Responsibilities

Design, develop, validate, and maintain predictive risk models using credit bureau and alternative datasets.

Build specialized models such as: bureau scorecards, collections/recovery models, client-specific models, income proxy/affordability models, and alternative‑data‑based risk models.

Test applicability of alternative data in the absence of bureau data.

Data Processing & Analytics

Extract, transform, and process large and complex datasets for modelling.

Develop efficient data pipelines using Python, SAS, SQL, and big data management tools.

Conduct rigorous feature engineering, data quality checks, and exploratory analysis.

Explore and integrate diverse datasets (payment, trade, telco, digital footprint, etc.).

Model Governance & Compliance

Ensure models comply with regulatory standards and internal governance.

Document methodology, assumptions, validation results, and monitoring plans.

Stakeholder Management

Work with banks, financial institutions, and fintech clients to deliver fit-for-purpose models.

Present complex results clearly to both technical and business stakeholders.

Manage end‑to‑end modelling projects, including scoping, design, development, and delivery.

Guide junior analysts and set best practices in modelling and analytics.

Requirements

Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Data Science, or related field.

5–7 years’ hands‑on experience in credit risk model development within financial institutions, fintechs, or credit bureaus.

Strong proficiency in statistical modelling techniques (logistic regression, survival models, ML techniques).

Proficiency in modelling and data processing tools: Python, SAS, SQL, big data management platforms.

Solid understanding of credit bureau and alternative data.

Experience in model governance, validation, and regulatory compliance.

Strong communication and stakeholder engagement skills.

KPIs / Success Measures

Delivery of models within agreed project timelines and client expectations.

Achieving or exceeding target predictive power benchmarks (e.g., GINI ≥ 45, KS ≥ 0.35, or as per client/regulatory standards).

Demonstrated uplift in client outcomes (e.g., higher approval rates, improved collections, lower bad rates).

Accuracy and robustness of models under backtesting and out-of-time validation.

Adoption rate of models by clients (implementation into production).

Compliance with regulatory, audit, and governance standards (zero critical findings).

Contribution to innovation through successful testing of new data sources and techniques.

Effective knowledge transfer and capability building within the team.

Nice‑to‑Have

Experience with AWS or other cloud‑based analytics environments.

Familiarity with advanced ML techniques (XGBoost, LightGBM, neural networks).

Regional experience in Asia or emerging markets.

What We Offer

Opportunity to shape the next generation of bureau and alternative data models.

Exposure to high-impact projects with banks, fintechs, and regulators.

Collaborative and innovative environment with strong career growth potential.

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