Credit Data Analytics Specialist

Job Purpose

To leverage advanced credit risk analytics, Expected Credit Loss (ECL) modelling support, and programming-based data analysis (Python / R) within the Credit Risk and Analysis Department to deliver high-quality insights that support credit risk monitoring, IFRS 9 compliance, and strategic portfolio decision-making in order to promote an enterprise-level, data-driven risk culture by integrating statistical analysis, automation, and visualization technologies to convert large and complex credit datasets into actionable intelligence for management, regulators, and stakeholders.

Responsibilities

• Design, develop, and maintain advanced Power BI dashboards covering: Credit portfolio composition and concentration risk, Asset quality and delinquency metrics, ECL metrics (Stage allocation, PD, LGD, EAD, overlays).
• Enhance dashboards by embedding outputs generated from Python or R analytics pipelines where relevant.
• Optimize DAX measures, data models, and performance for large-scale credit datasets.
• Conduct sophisticated analysis using Python and/or R, including:

• Credit migration and roll-rate analysis
• Vintage analysis and cohort tracking
• Stress testing and scenario analysis
• Sensitivity, simulation and impact analysis on ECL
• Other related credit risk report.

• Support ECL model monitoring by:

• Performing trend and back-testing analysis
• Identifying model drift, outliers, and unusual behaviours from ECL model data include PD, LGD, MEVs.

• Apply statistical and analytical techniques such as:

• Regression analysis
• Probability distributions
• Time-series analysis
• Document methodologies and analytical assumptions clearly to ensure auditability.

• Build and maintain automated data pipelines using:

• Python (pandas, numpy, scipy, matplotlib, seaborn)
• R (tidyverse, data.table, ggplot2).

• Integrate data from:

• Core banking systems
• Credit systems
• ECL model platforms
• External macroeconomic datasets (when applicable).

• Design and maintain ETL workflows using:

• Power Query
• SQL
• Python or R scripts.

• Reduce manual Excel-based processes by replacing them with reproducible code-based analytics.
• Implement data validation, reconciliation, and version control to ensure accuracy and reliability.
• Continuously enhance expertise in:

• Python and R for financial and risk analytics
• Credit risk and ECL methodologies
• Data visualization and automation tools.

• Explore emerging analytics techniques (e.g., machine learning applications for credit risk where applicable).

Qualifications

• Bachelor’s degree in finance, Accounting, Information System and/or other related fields is required.
• Minimum two years of experiences in Finance, Accounting, and Business Analytics, preferably in financial services.
• Strong proficiency in Python and/or R for data analysis and automation.
• Hands-on experience with pandas, numpy, scipy, matplotlib, seaborn (Python), tidyverse, data.table, ggplot2 (R).
• Advanced skills in: Power BI (DAX, data modelling, performance optimization), Power Query, SQL
• Strong understanding of Credit risk fundamentals, IFRS 9 / ECL concepts, Loan portfolios and asset quality metrics.
• Experience developing executive-level dashboards and analytical reports.