Requirements

Expected technologies

Python

SQL

Operating system

Windows

Our requirements

• 3+ years of experience in a Data Scientist or ML Engineer role in a regulated or financial environment
• Proven experience with credit risk modeling including logistic regression, scorecards, and survival models
• Strong coding skills in Python and experience with libraries such as scikit-learn, XGBoost, pandas, SHAP
• Proficiency in SQL for data extraction, transformation, and analysis
• Understanding of statistical concepts and model evaluation techniques
• Familiarity with credit lifecycle data including payments, delinquencies, and account activity
• Experience with model interpretability tools and practices
• Ability to work collaboratively with engineering and risk teams
• Strong communication skills with the ability to simplify complex model logic for non-technical stakeholders
• Experience working in environments that require model documentation and validation

Optional

• Familiarity with regulatory frameworks such as IFRS 9, ECL, or Basel III
• Hands-on experience with real-time or batch scoring pipeline integration
• Knowledge of Markov chains in the context of credit risk transitions
• Exposure to model monitoring and drift detection tools

Your responsibilities

• Developing predictive models for credit scoring, loan deterioration, and time-to-default
• Performing data exploration and feature engineering using behavioral and transactional credit data
• Ensuring model performance, robustness, and interpretability using statistical and ML-based metrics
• Collaborating with Data Engineering teams to integrate models into production environments
• Supporting the documentation, audit, and validation processes for regulatory compliance
• Applying advanced ML techniques such as XGBoost, LightGBM, and survival models to risk problems
• Monitoring and retraining models to ensure long-term reliability and compliance
• Presenting findings and explaining model logic to risk, compliance, and audit stakeholders