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Actuarial Data Scientist

HALA
Riyadh, KSA
Full Time
Mid
Hybrid
2 days ago
PythonSQLStatistical ModellingMachine LearningRegressionClassification Models
Free

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Role Summary

  • Hala Financing is looking for an Actuarial Data Scientist to join the Data team and support the development of our credit engine, risk models, and portfolio monitoring capabilities.
  • The role will focus on predicting probability of default, improving credit decisioning, enhancing risk segmentation, and building data driven models that support responsible growth in SME lending.
  • The ideal candidate combines actuarial thinking, credit risk modelling, machine learning, and strong business judgment.

Key Responsibilities

  • Build, validate, and improve models for probability of default, credit scoring, affordability, delinquency prediction, and customer risk segmentation.
  • Analyze historical repayment behavior, first payment failure, delinquency trends, vintage curves, and default patterns.
  • Support the enhancement of Hala Financing’s credit engine by identifying stronger predictive variables and decision rules.
  • Develop early warning indicators to detect customers likely to delay, default, or underperform.
  • Monitor portfolio performance across cohorts, channels, customer segments, loan products, tenure, ticket size, and repayment behavior.
  • Build dashboards and analytical frameworks to track approval quality, disbursement performance, default rates, roll rates, collections performance, and portfolio risk.
  • Run scenario analysis and stress testing to assess the impact of growth, pricing, approval policy, and macroeconomic changes on portfolio performance.
  • Support management reporting for credit performance, investor reporting, and internal risk committees.
  • Use statistical and machine learning techniques to improve credit decisioning and default prediction.
  • Work with structured and alternative data sources, including transaction data, merchant behavior, repayment history, business activity, and external data where available.
  • Design experiments and champion/challenger tests to evaluate credit policy changes.
  • Partner with Data Engineering to improve data quality, feature availability, model monitoring, and automation.

Required Qualifications

  • Bachelor’s degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Finance, or a related quantitative field; Master’s degree preferred.
  • 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/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 translate complex analytical findings into simple business recommendations.
  • Strong communication skills and ability to work with both technical and non technical stakeholders.

Key Success Measures

  • Improved accuracy of default prediction and credit risk segmentation.
  • Reduced first payment failure and early delinquency rates.
  • Stronger credit engine decisioning and approval quality.
  • Clear portfolio monitoring and early warning indicators.
  • Better balance between loan growth, risk, profitability, and capital efficiency.

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