Credit Risk Data Analyst

Akulaku Indonesia; Jakarta Pusat; On-site; IDR 216,000,000 - 384,000,000; Full time

Job summary

Akulaku Indonesia is seeking a data analytics professional to monitor credit portfolio performance, conduct data exploration, and generate actionable risk insights. You will support policy adjustments, build dashboards, and collaborate with risk modelers and cross-functional teams to drive data-driven decisions.

The role requires strong SQL, Python or R, and experience with BI tools; you will translate complex numbers into business strategies and contribute to risk management in a fast-growing

Qualifications

• Bachelor’s degree in Statistics, Mathematics, Economics, or a quantitative field.
• Minimum 2 years of experience in Data Analytics, Risk Management, or Credit Scoring in fintech or banking.
• Highly proficient in SQL (mandatory) for complex data manipulation, querying, and extraction.
• Familiarity with Python or R (libraries such as Pandas, Scikit-learn, and XGBoost).
• Proficient in creating dashboards using Superset, Tableau, PowerBI, or Metabase. Advanced level (VBA, Power Query, Pivot Tables).
• Ability to translate complex data into actionable business strategies and risk policies.
• Strong problem-solving skills with a keen eye for detail and data anomalies.

Responsibilities

• Portfolio Monitoring: Analyze credit portfolio performance (NPLs, delinquency rates, vintage analysis, roll rates) and prepare periodic risk trend reports.
• Data Exploration & Insight Generation: Conduct deep-dive analysis into customer behavior and credit data to identify risk patterns, anomalies, and opportunities.
• Policy & Strategy Recommendations: Provide data-driven recommendations for adjustments to credit policies (e.g., cut-off scores, credit limits, pricing strategies).
• Reporting & Dashboarding: Create, maintain, and automate dashboards using BI tools to monitor risk KPIs for stakeholders (Risk, Product, Business).
• Cross-functional Collaboration: Support Risk Modelers/Data Scientists in the implementation and monitoring of credit scoring models.

Skills

SQL

Python

R

Data analysis

Risk management

Credit scoring

Education

Bachelor’s degree in Statistics

Bachelor’s degree in Mathematics

Bachelor’s degree in Economics

Quantitative field degree

Tools

Superset

Tableau

PowerBI

Metabase

Power Query

VBA

Pivot Tables

XGBoost

Job description

• Portfolio Monitoring: Analyze credit portfolio performance (NPLs, delinquency rates, vintage analysis, roll rates) and prepare periodic risk trend reports.
• Data Exploration & Insight Generation: Conduct deep-dive analysis into customer behavior and credit data to identify risk patterns, anomalies, and opportunities.
• Policy & Strategy Recommendations: Provide data-driven recommendations for adjustments to credit policies (e.g., cut-off scores, credit limits, pricing strategies).
• Reporting & Dashboarding: Create, maintain, and automate dashboards using BI tools to monitor risk KPIs for stakeholders (Risk, Product, Business).
• Cross-functional Collaboration: Support Risk Modelers/Data Scientists in the implementation and monitoring of credit scoring models.

Responsibilities

• Portfolio Monitoring: Analyze credit portfolio performance (NPLs, delinquency rates, vintage analysis, roll rates) and prepare periodic risk trend reports.
• Data Exploration & Insight Generation: Conduct deep-dive analysis into customer behavior and credit data to identify risk patterns, anomalies, and opportunities.
• Policy & Strategy Recommendations: Provide data-driven recommendations for adjustments to credit policies (e.g., cut-off scores, credit limits, pricing strategies).
• Reporting & Dashboarding: Create, maintain, and automate dashboards using BI tools to monitor risk KPIs for stakeholders (Risk, Product, Business).
• Cross-functional Collaboration: Support Risk Modelers/Data Scientists in the implementation and monitoring of credit scoring models.

Requirements

• Minimum bachelor’s degree in Statistics, Mathematics, Actuarial Science, Economics, Industrial Engineering, Computer Science, or another quantitative field.
• Minimum 2 years of experience in Data Analytics, Risk Management, or Credit Scoring in the Banking, Fintech Lending, or Multifinance industries.
• Highly proficient in SQL (mandatory) for complex data manipulation, querying, and extraction.
• Familiarity with Python or R (especially libraries such as Pandas, Scikit-learn, and XGBoost).
• Proficient in creating dashboards using Superset, Tableau, PowerBI, or Metabase.Advanced level (VBA, Power Query, advanced Pivot Tables).
• Ability to translate complex data and numbers into actionable business strategies and risk policies.
• Strong problem-solving skills with a keen eye for detail and data anomalies.
• Ability to clearly explain data insights and risk trends to non-technical stakeholders.