Credit Risk Data Analyst

Akulaku Group; Jakarta Pusat; On-site; IDR 180,000,000 - 240,000,000; Full time

Job summary

Akulaku Group in Jakarta seeks a Data Analytics & Risk Analyst to monitor credit portfolio performance, analyze customer behavior, and help shape risk policies. You will build dashboards and reports to track NPLs, delinquency, vintage and roll rates, collaborating with risk modelers and data scientists.

The role requires strong SQL skills, experience with Python or R, and a proven ability to translate data into actionable risk insights for stakeholders.

Qualifications

• 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.

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

Dashboards

Data storytelling

Analytical thinking

Problem solving

Education

Bachelor's degree in Statistics

Bachelor's degree in Mathematics

Actuarial Science

Economics

Industrial Engineering

Computer Science

Tools

Tableau

PowerBI

Superset

Metabase

Excel

VBA

Power Query

Pivot Tables

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.

• 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.