Credit Risk Modelling Data Scientist (Fintech)

TL;DR

Credit Risk Modelling Data Scientist (Fintech): Building and improving credit risk models, decisioning systems, and portfolio monitoring capabilities for SME lending with an accent on probability of default, credit scoring, and risk segmentation. Focus on designing machine learning models, running stress tests and champion/challenger experiments, and translating portfolio insights into practical credit policy decisions.

Location: Riyadh, Saudi Arabia

Company

hirify.global is a Saudi Central Bank-licensed fintech providing payment, financing, logistics, and business management solutions for SMEs across the MENAP region.

What you will do

• Build, validate, and improve models for probability of default, credit scoring, affordability, delinquency prediction, and customer risk segmentation.
• Analyze repayment behavior, first-payment failures, delinquency trends, vintage curves, and default patterns.
• Enhance the credit engine through predictive variables, decision rules, early-warning indicators, and champion/challenger testing.
• Monitor portfolio performance across cohorts, channels, customer segments, products, and repayment behavior.
• Develop dashboards, scenario analyses, stress tests, and management reporting for credit and portfolio performance.
• Partner with Credit, Risk, Product, Collections, Finance, Business, and Data Engineering teams to turn analytical findings into business actions.

Requirements

• 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 Python and SQL skills.
• Experience with statistical modelling, machine learning, regression, classification, decision trees, gradient boosting, model validation, and performance monitoring.
• Ability to communicate complex analytical findings as clear recommendations to technical and non-technical stakeholders.

Nice to have

• Master’s degree in a quantitative discipline.

Culture & Benefits

• Inclusive and diverse culture with flexibility across remote, in-office, and hybrid work setups.
• Competitive compensation package with potential share participation.
• Regular training and an annual learning stipend.
• International environment with colleagues from more than 30 nationalities across 7 countries.
• Autonomy, mentoring, responsibility, and challenging goals in a hyper-growth environment.