Credit Risk Modelling Data Scientist

About HALA

Role Overview

Credit Risk Modelling

• Build, validate, and improve models for probability of default, credit scoring, affordability, delinquency prediction, and customer risk segmentation.
• Analyze repayment behavior, first-payment failure, delinquency trends, vintage curves, and default patterns.
• Identify predictive variables and decision rules to enhance the credit engine.
• Develop early-warning indicators for customers likely to delay, default, or underperform.

Portfolio Analytics

• Monitor portfolio performance across cohorts, channels, customer segments, loan products, tenure, ticket size, and repayment behavior.
• Build dashboards and analytical frameworks for approval quality, disbursement performance, default rates, roll rates, collections, and portfolio risk.
• Run scenario analysis and stress testing for changes in growth, pricing, approval policy, and macroeconomic conditions.
• Support management, investor, and internal risk committee reporting.

Data Science and Machine Learning

• Use statistical and machine learning techniques to improve credit decisioning and default prediction.
• Work with transaction data, merchant behavior, repayment history, business activity, and external data where available.
• Design experiments and champion-challenger tests for credit policy changes.
• Partner with Data Engineering on data quality, feature availability, model monitoring, and automation.

Business Partnership

• Work with Credit, Risk, Product, Collections, Finance, and Business teams to translate questions into analytical solutions.
• Recommend changes to credit policy, approval rules, risk appetite, and portfolio growth.
• Balance growth, profitability, and risk by translating data insights into practical business actions.

Required Qualifications

• Bachelor’s degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Finance, or a related quantitative field.
• 3-6 years of experience in actuarial analytics, credit risk, lending analytics, banking, fintech, insurance, or financial modelling.
• Strong understanding of probability of default, credit scoring, portfolio risk, delinquency, loss forecasting, and cohort or vintage analysis.
• Strong skills in Python and SQL.
• Experience with statistical modelling, machine learning, regression, classification models, decision trees, gradient boosting, model validation, and performance monitoring.
• Ability to communicate complex analytical findings as simple business recommendations.
• Strong communication skills with technical and non-technical stakeholders.
• A relevant master’s degree is preferred.

What HALA Offers

• HALA describes an inclusive culture with remote, in-office, and hybrid work setups.
• The company offers competitive compensation, potential shares, training, an annual learning stipend, mentoring, autonomy, and challenging goals.
• The team includes more than 30 nationalities across 7 countries.