Senior / Lead Data Scientist – Credit Risk Modeling

Since 2005, Klarna has been on a mission to reimagine how consumers spend and save in their daily lives. We help people save time and money and reduce financial worry. With over 85 million global active users and 2.5 million transactions per day, our vision is a world where Klarna empowers everyone, everywhere, through seamless financial and commerce experiences—as a personalized, trusted assistant making financial empowerment effortless.

Over 575,000 global retail partners, including H&M, Saks, Sephora, Macys, IKEA, Expedia Group, and Nike have integrated Klarna's innovative technology to deliver a seamless shopping experience online and in-store.

About Us

With over 85 million global active users and 2 million transactions per day, Klarna is on the way to becoming the world’s favorite way to shop. To help us get there, we’re assembling an unparalleled global talent team—accelerating individual careers, and disrupting entire industries. We’re looking for people ready to achieve the extraordinary and embrace our bold ambitions as we shape the future of payments and fintech. Will you join us?

What You Will Do

As a Lead Data Scientist in Credit Risk Modeling, you will be part of the team shaping 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 dynamic 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 our 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

Closing

To ensure fairness and maintain global market competitiveness, each role in a specific location has a set base salary. During the recruitment process, we will assess your skills and experience to determine which role is the best fit for you.

Additionally, you may qualify for our Contribution-Based Reward (CBR) program, which recognizes and rewards significant contributions to our success.

Please include a CV in English.