Credit Data Analytics Specialist

About the role

The Credit Risk and Analysis Department is looking for a Credit Data Analytics Specialist to work from the Head Office in Phnom Penh. The role uses advanced credit risk analytics, Expected Credit Loss (ECL) modelling support and Python/R-based data analysis to turn large credit datasets into actionable intelligence for management, regulators and stakeholders, supporting credit risk monitoring, IFRS 9 compliance and portfolio decisions.

Key responsibilities

• Design, develop and maintain Power BI dashboards on credit portfolio composition, asset quality and delinquency, and ECL metrics (stage allocation, PD, LGD, EAD, overlays)
• Embed Python or R analytics outputs in dashboards and optimize DAX measures, data models and performance
• Analyze credit data with Python and/or R: migration and roll-rate analysis, vintage and cohort analysis, stress testing, sensitivity and simulation analysis
• Support ECL model monitoring through trend and back-testing analysis, and identify model drift, outliers and unusual behaviour
• Apply regression, probability distributions and time-series analysis; document methods and assumptions clearly
• Build automated data pipelines in Python (pandas, numpy, scipy, matplotlib, seaborn) and R (tidyverse, data.table, ggplot2), integrating core banking, credit system, ECL platform and macroeconomic data
• Maintain ETL workflows with Power Query and SQL, reduce manual Excel work, and implement validation, reconciliation and version control

Required profile

• Bachelor's degree in finance, accounting, information systems or a related field
• At least two years of experience in finance, accounting and business analytics, preferably in financial services
• Experience building executive-level dashboards and analytical reports
• Understanding of credit risk fundamentals, IFRS 9/ECL concepts, loan portfolios and asset quality metrics

Required skills

• Strong proficiency in Python and/or R
• Hands-on experience with pandas, numpy, scipy, matplotlib, seaborn, tidyverse, data.table and ggplot2
• Advanced Power BI (DAX, data modelling, performance optimization), Power Query and SQL