Manager Data Analytics and Modelling

CTOS; Petaling Jaya; On-site; MYR 100,000 - 150,000; Full time

Job summary

A leading credit bureau is seeking an experienced Analytics Modelling & Analytics Manager to lead the development and validation of advanced risk models. Your role will focus on utilizing both traditional and alternative datasets to enhance decision-making across the credit lifecycle. Collaboration with banks and fintech clients will be essential, along with managing projects from conception to delivery. Strong technical and stakeholder management skills are required. Potential for significant impact in a collaborative work environment.

Benefits

Collaborative and innovative environment

Strong career growth potential

Qualifications

• 5–7 years’ hands‑on experience in credit risk model development within financial institutions.
• Strong proficiency in statistical modelling techniques including logistic regression.
• Experience in model governance, validation, and regulatory compliance.

Responsibilities

• Design and develop predictive risk models using diverse datasets.
• Ensure compliance with regulatory standards and internal governance.
• Manage end‑to‑end modelling projects and guide junior analysts.

Skills

Statistical modelling techniques

Data processing skills

Strong communication

Stakeholder engagement

Education

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

Tools

Python

SAS

SQL

Big data management tools

Job description

Role Overview

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

Risk Model Development

• 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.

Team & Project Leadership

• 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.