Analytics Modelling

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.