Senior/Lead Data Scientist -Credit & Finance Model Validation

Job-ID: 26e3280a-5701-4dc6-979e-7ab81c0e919cLocation: Stockholm; LondonSalary: 78293 - 111960 GBPType: Full timePosted: 2026-04-27Contact: ivy.chiang@klarna.comWhat you will do Perform independent end-to-end validation of credit risk (e.g., underwriting and limit management), finance (provisioning, offloading, profitability), and other models, rigorously reviewing and challenging all aspects: conceptual soundness, data integrity, feature engineering and selection, training and testing, regulatory compliance and fairness, documentation, deployment, monitoring and business impact. Independently replicate the model development process where necessary and conduct challenger analyses. Collaborate closely with first-line data scientists, machine learning (ML) engineers, and product stakeholders to understand models’ business context and ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and regulatory expectations. Drive the continuous enhancement of agentic AI tools that support and accelerate model validation by automating documentation and code review, surfacing cross-source inconsistencies, streamlining challenger analysis, etc. Stay up-to-date with emerging trends in credit and finance modelling and AI/ML technologies. Maintain robust model risk management frameworks, policies, and procedures in line with evolving regulatory expectations and industry best practices. Who you are Advanced degree (Master’s or PhD) in a quantitative field such as data science, statistics, mathematics, computer science, physics, or engineering; or equivalent experience. 3+ years of hands-on experience in credit risk and/or IFRS9/CECL impairment modeling. Strong technical expertise in statistical and machine learning models, with a deep understanding of credit risk and/or IFRS9/CECL provisioning models. Hands-on experience with programming languages and tools commonly used in data science, such as Python, SQL, Spark, and AWS. Excellent analytical, problem-solving, and decision-making abilities. A passion for innovation and staying at the forefront of data science and risk management. Strong communication and stakeholder management skills, with the ability to convey complex technical information to non-technical audiences. Knowledge of regulatory requirements and expectations for model risk management. Awesome to have Experience with Buy Now Pay Later (BNPL), credit cards, personal loans, and payments products. Experience mentoring junior validators or leading validation reviews. Experience building agentic AI workflows and familiarity with AI governance frameworks and emerging AI regulatory requirements.