Data Scientist – Credit Analytics

Electrum; Jakarta Selatan; On-site; IDR 200.000.000 - 300.000.000; Full time

Job summary

A leading financial technology company is seeking a Data Scientist – Credit Analytics to build and analyze credit risk models. The role involves working closely with various teams to translate data insights into actionable credit decisions. Candidates should have a degree in a related field and experience in credit analytics. Strong proficiency in Python or R is required, along with a solid understanding of statistical modeling. This position offers the opportunity to significantly impact the company's credit risk management processes.

Qualifications

• 2-5 years of experience in credit analytics, risk modeling, or financial data science.
• Solid understanding of statistical modeling, machine learning, and predictive analytics.
• Experience working with structured datasets in SQL.

Responsibilities

• Develop and maintain credit scoring models and decision frameworks.
• Analyze customer and behavioral data to assess creditworthiness.
• Design dashboards to monitor portfolio health and early warning indicators.

Skills

Credit analytics

Data analysis

Machine learning

Python

R

SQL

Education

Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Economics, Engineering

Tools

Looker

Tableau

Power BI

Job description

About the Role

We are looking for a Data Scientist – Credit Analytics to build, analyze, and optimize credit risk models that support Electrum’s financing, rent-to-own, and partner credit programs. In this role, you will work closely with Risk, Finance, Product, and Operations teams to translate data into actionable insights that drive better credit decisions, portfolio performance, and customer outcomes.

You will play a key role in developing data-driven frameworks for credit scoring, approval, monitoring, and loss mitigation across Electrum’s ecosystem.

What You Will Do

Credit Modeling & Analytics

• Develop and maintain credit scoring models, risk segmentation, and decision frameworks.

• Analyze customer, transaction, and behavioral data to assess creditworthiness and default risk.

• Build and validate predictive models for approval rate, delinquency, default, and recovery.

• Monitor portfolio performance metrics such as NPL, PD, LGD, ECL, and vintage analysis.

Data Analysis & Insights

• Conduct deep-dive analyses to identify drivers of credit performance and risk trends.

• Perform cohort, funnel, and behavioral analysis to improve underwriting and credit policy.

• Translate complex data findings into clear insights and recommendations for stakeholders.

• Support A/B testing and experimentation for credit rules, pricing, and policy changes.

Risk Strategy & Decision Support

• Partner with Risk and Business teams to define credit policies, cut-offs, and eligibility rules.

• Provide data support for credit strategy initiatives including limit setting, pricing, and collections.

• Design dashboards and monitoring tools to track portfolio health and early warning indicators.

Data Engineering & Governance

• Work with Data Engineering teams to ensure data quality, availability, and reliability.

• Define data requirements, feature engineering logic, and data documentation standards.

• Ensure compliance with data governance, privacy, and regulatory standards.

What You Bring

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, Engineering, or related field.

• 2–5 years of experience in credit analytics, risk modeling, or financial data science.

• Strong proficiency in Python or R for data analysis and modeling.

• Solid understanding of statistical modeling, machine learning, and predictive analytics.

• Experience working with structured datasets (SQL, data warehouses).

• Knowledge of credit risk concepts such as PD, LGD, EAD, NPL, IFRS 9 / PSAK 71 is a strong advantage.

• Ability to clearly communicate insights to non-technical stakeholders.

Nice to Have

• Experience in fintech, lending, BNPL, rent-to-own, or consumer finance.

• Familiarity with alternative data sources (telemetry, transactional, behavioral data).

• Experience with BI tools (Looker, Tableau, Power BI).

• Exposure to model governance, validation, or regulatory reporting.