Principal Data Scientist - Model Risk

• Lead Klarna's Model Risk and Model Validation function, sitting within the second line of defense as part of Risk Control.

• Establish and maintain robust frameworks, policies, and procedures for model risk governance, in line with regulatory requirements and industry best practices.

• Manage the independent validation of a wide range of models, including but not limited to:

• Credit scoring models

• Fraud detection models

• IFRS9 Expected Credit Loss (ECL) provisioning models

• Review and challenge model development methodologies, assumptions, and implementation processes to ensure their appropriateness, robustness, and transparency.

• Conduct detailed assessments of model performance, accuracy, and limitations, providing actionable recommendations for improvements.

• Collaborate closely with first-line teams, such as data scientists, model developers, and business units, to ensure alignment and effective communication of model-related risks.

• Stay updated on evolving regulatory expectations (e.g., IFRS9, Basel III/IV) and emerging trends in machine learning and AI model validation.

• Build and mentor a high-performing team of model validation professionals, fostering a culture of excellence, collaboration, and continuous learning.

• Present findings, insights, and reports to senior management, audit committees, and regulators.

• Proven leadership skills, with experience building and managing high-performing teams.

• Experience interacting with regulators and senior management.

• Advanced degree (Master's or PhD) in a quantitative discipline such as Data Science, Statistics, Mathematics, Computer Science, or a related field.

• Extensive experience (7+ years) in model development, validation, or risk management within the financial services sector.

• Strong technical expertise in statistical and machine learning models, with a deep understanding of credit scoring, fraud detection, and IFRS9 ECL models.

• Hands-on experience with programming languages and tools commonly used in data science, such as Python and SQL.

• In-depth knowledge of regulatory requirements and expectations for model governance and validation.

• Exceptional analytical, problem-solving, and decision-making abilities.

• Excellent communication and stakeholder management skills, with the ability to convey complex technical information to non-technical audiences.

• A passion for innovation and staying at the forefront of data science and risk management advancements.

Please include a CV in English.

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