Senior/Lead Data Scientist -Credit Risk Modeling
Most applications go out cold — see where you stand first. No sign-up to start.
Don't just apply. Show up ready.
Olive works from this exact posting — no sign-up to start.
At a glance
Job overview
Klarna is hiring a Senior/Lead Data Scientist -Credit Risk Modeling. Klarna seeks a Lead Data Scientist to design and maintain real-time probability of default models for consumer credit, integrating statistical and machine learning methods, ensuring regulatory compliance, and advancing explainability with LLMs. The role involves translating insights into credit policies, influencing cross‑functional decisions, and mentoring junior team members while shaping the company’s long‑term modeling strategy.
Key focus areas include Design and maintain real-time PD models using statistical and ML approaches, Develop calibration frameworks and ensure regulatory and fairness compliance, and Integrate models into underwriting and economic return optimization pipelines.
Successful candidates bring 5+ Years Credit Risk Modeling Experience and CV In English. Important skills include PD Model Development, PD Model Validation, Calibration Techniques, Python, SQL, and XGBoost. Preferred (not required): LLMs For Feature Extraction, Integrating Third-Party Credit Bureau Data, Champion/Challenger Model Frameworks, and A/B Testing Infrastructure.
Skills & qualifications
Skills
Qualifications
Full job description
At 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. What you'll do 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 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. Who you are 5+ years' experience in credit risk modeling 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 modeling and current trends in ML and credit analytics. Awesome to have 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 modeling, including cost-of-capital and loss metrics. Please include a CV in English Curious to learn more about Klarna and what it's like to work here? Explore our career site!
You've read the whole posting — now see how you match it.