As a Lead Data Scientist within credit risk modeling, you will shape Klarna’s next‑generation consumer‑level credit scoring and portfolio valuation models. You’ll design and maintain real‑time PD (Probability of Default) models using statistical and ML approaches, integrating them into frameworks for underwriting and economic return optimisation.
You’ll develop calibration frameworks, ensure compliance with regulatory and fairness standards, and explore novel methodologies—including LLMs for explainability and feature engineering. Collaborating with cross‑functional teams, you’ll translate modelling insights into strategic credit policies and business value, while mentoring junior team members and contributing to Klarna’s long‑term modelling vision.
Who you are- 5+ years’ experience in credit risk modelling for consumer lending, credit cards, or BNPL.
- Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques.
- Advanced Python and SQL skills; familiar with XGBoost, scikit–learn, pandas, MLFlow.
- Experience with explainability frameworks such as SHAP, LIME, PDP.
- Ability to communicate technical concepts clearly and influence cross‑functional decisions.
- Familiarity with real‑time modelling and current trends in ML and credit analytics.
- Hands‑on experience using LLMs to extract features from unstructured data (e.g. customer communications, credit applications).
- Knowledge of integrating third‑party credit bureau data into production models.
- Understanding of champion/challenger model frameworks and A/B testing infrastructure.
- Exposure to loan‑level economic modelling, including cost‑of‑capital and loss metrics.