Credit Risk Modelling Data Scientist 

HALA
Riyadh
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: bachelorsSkills: ["Communication","Stakeholder management","Business judgment"]

Build and validate credit risk and default prediction models to improve probability of default, credit scoring, affordability, delinquency prediction, and customer segmentation for SME lending. Monitor portfolio performance across cohorts and channels using dashboards and analytical frameworks, and run scenario analysis and stress testing. Apply statistical and ML techniques using structured and alternative data, partnering with Data Engineering and collaborating with Credit, Risk, Product, Collections, and Finance to translate insights into credit decisioning and policy recommendations.

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FursaFursa
HALA
HALA
1 month ago

Credit Risk Modelling Data Scientist 

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Source: Company careers pageValidated by: Fursa AI
Last checked: 20 hours agoStatus: Live

Job Summary

Build and validate credit risk and default prediction models to improve probability of default, credit scoring, affordability, delinquency prediction, and customer segmentation for SME lending. Monitor portfolio performance across cohorts and channels using dashboards and analytical frameworks, and run scenario analysis and stress testing. Apply statistical and ML techniques using structured and alternative data, partnering with Data Engineering and collaborating with Credit, Risk, Product, Collections, and Finance to translate insights into credit decisioning and policy recommendations.
Location: Riyadh
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

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 to refine predictive variables and decision rules.
  • •Monitor portfolio performance across cohorts, channels, customer segments, loan products, and repayment behavior using dashboards and analytical frameworks.
  • •Run scenario analysis and stress testing to assess the impact of growth, pricing, approval policy, and macroeconomic changes on portfolio performance.
  • •Apply statistical and machine learning techniques using structured and alternative data sources; partner with Data Engineering to improve data quality, feature availability, model monitoring, and automation.

Pay and Benefits

Equity and Bonus:Equity
Perks:Learning BudgetEquity

Key Requirements

  • •Bachelor’s degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Finance, or a related quantitative field (Master’s 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 and machine learning (regression, classification, decision trees, gradient boosting) plus model validation and performance monitoring.
Experience:FintechCredit riskLending analyticsBankingInsuranceSME lending
Education:Bachelor's
Skills:CommunicationStakeholder managementBusiness judgment
Languages:English
Tech Stack:PythonSQLMachine learningRegressionClassification modelsDecision treesGradient boosting

Company Brief

HALA
Provides a mobile-first neobanking platform delivering digital financial services such as virtual cards, payments, and expense management to consumers and small businesses, aiming to simplify everyday banking and cross-border transactions in the region.
Industry: Neobanking
Website