Senior/Lead Data Scientist -Credit Risk Modeling

ppAt Klarna, our credit risk models sit at the heart of how we underwrite and price risk for millions of consumers globally. We're looking for a Lead Data Scientist to help shape the next generation of consumer-level credit scoring and portfolio valuation models. /ph3What You'll Do /h3pAs 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 optimization. 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 modeling insights into strategic credit policies and business value, while mentoring junior team members and contributing to Klarna's long-term modeling vision. /ph3Who You Are /h3ulli5+ years' experience in credit risk modeling for consumer lending, credit cards, or BNPL. /liliDeep proficiency in PD model development and validation, with strong knowledge of calibration techniques. /liliAdvanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow. /liliExperience with explainability frameworks such as SHAP, LIME, PDP. /liliAbility to communicate technical concepts clearly and influence cross-functional decisions. /liliFamiliarity with real-time modeling and current trends in ML and credit analytics. /li /ulh3Awesome to have /h3ulliHands-on experience using LLMs to extract features from unstructured data (e.g., customer communications, credit applications). /liliKnowledge of integrating third-party credit bureau data into production models. /liliUnderstanding of champion/challenger model frameworks and A/B testing infrastructure. /liliExposure to loan-level economic modeling, including cost-of-capital and loss metrics. /li /ul /p #J-18808-Ljbffr