Senior Data Scientist - Credit Risk & Provisioning Models @ Klarna

Senior Data Scientist - Credit Risk & Provisioning Models

Engineering · Stockholm · Full-time · kr 611,050 SEK - kr 843,151 SEK

OverviewApplication

What You'll Do

• Develop and maintain credit risk models for Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and lifetime Expected Credit Loss (ECL) across multiple regions and products.
• Train gradient boosting models(LightGBM, XGBoost) for credit risk prediction with rigorous calibration, backtesting, and out-of-time validation.
• Design and implement vectorized models for computing forward-looking lifetime ECL estimates, incorporating macroeconomic scenarios and discounting.
• Perform feature engineeringon credit datasets including payment behavior, delinquency patterns, bureau credit scores, and transactional features.
• Manage the full model lifecycle using MLflow for experiment tracking, model versioning, and registry, ensuring reproducibility and complete audit trails.
• Build and maintain model monitoring to track performance, stability, and drift across markets, producing dashboards and automated alerts.
• Develop macro-overlay modelsthat incorporate macroeconomic variables (unemployment, GDP, interest rates) into forward-looking credit loss projections.
• Support fair value estimation and coverage rate analysis for debt sale pricing and capital management decisions.
• Run end-of-month production scoring - loading trained models, scoring exposure data at scale on cloud compute, and validating ECL outputs.
• Maintain model documentation and support audit reviews, regulatory inquiries, and model validation exercises.
• Collaborate with Data Engineers to define feature requirements, validate pipeline outputs, and ensure model inputs are accurate and timely.
• Present results to senior stakeholders including Finance leadership, auditors, and regulatory reviewers.

Who you are
* 3+ years of experience in a Data Science, Quantitative Analyst, or Credit Risk Modeling role.
* Strong Python skills for modeling, analysis, and production code (pandas, NumPy, scikit-learn).
* Experience with gradient boosting frameworks- LightGBM, XGBoost, or CatBoost.
* Solid statistical foundations - probability theory, hypothesis testing, regression, time series, survival analysis, or transition matrices.
* SQL proficiency - complex analytical queries on a data warehouse for feature extraction, validation, and ad-hoc analysis.
* Model lifecycle experience - training, hyperparameter tuning, validation, deployment, and monitoring.
* Experience with experiment tracking tools such as MLflow, Weights & Biases, or similar.
* Strong communication skills - ability to explain model behavior, limitations, and results to non-technical stakeholders.

Awesome to have

• Credit risk modeling experience - PD, LGD, EAD, transition matrices, vintage analysis, or roll-rate models.
• IFRS 9 / CECL knowledge - staging criteria, lifetime vs. 12-month ECL, forward-looking adjustments, macroeconomic overlays.
• Familiarity with model interpretabilitytechniques (SHAP, feature importance, partial dependence plots).
• Experience with Bayesian optimization for hyperparameter tuning.
• Exposure to Numba or vectorized computation for high-performance model calculations.
• Familiarity with fair value or pricing models for consumer credit portfolios.
• Understanding of cloud infrastructure (AWS S3, Batch, Docker) for model deployment and scoring.
• Background in fintech, banking, or consumer lending.

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