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.