(5 st) Data Science Manager
Arbetsgivare / Ort:Klarna Bank AB / STOCKHOLM
Omfattning / Varaktighet:Tillsvidareanställning (inkl. eventuell provanställning) / Tills vidare
Lönetyp: Fast månads- vecko- eller timlön
Ansök senast 30 augusti (2 månader kvar)
Publicerad: 2026-05-15
Källa: Arbetsförmedlingen
Om jobbet
What you will do
- Perform independent end-to-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models.
- Review and challenge first-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit-learn, LightGBM, graph models, anonaly detection techniques, GenAI components).
- Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies.
- Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, fraud rate capture, business impact trade-offs).
- Evaluate model stability, drift detection, retraining strategies, and production monitoring practices.
- Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations.
- Review large-scale transaction datasets and feature pipelines (e.g., >100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias.
- Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy.
- Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI-based systems.
- Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda).
- Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python-based validation libraries).
- Collaborate closely with first-line fraud data scientists, ML engineers, product, and business stakeholders to 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 external regulatory expectations.
- Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management.
Who you are
- Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering.
- 3+ years of hands-on experience in fraud-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc).
- Strong expertise in machine learning methods used in fraud detection, including tree-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques.
- Deep understanding of the end-to-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage.
- Strong programming skills in Python and SQL; experience with PySpark/Spark and large-scale data processing.
- Experience building agentic AI workflows.
- Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows.
- Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations.
- Experience assessing model bias, fairness, explainability, and privacy risks.
- Excellent analytical thinking and structured problem-solving skills, with the ability to assess complex models and clearly articulate risks and limitations.
- Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non-technical audiences.
- Ability to work independently while constructively challenging first-line teams in a collaborative manner.
Awesome to have
- Experience in BNPL, credit cards, payments, or other transaction-heavy financial products.
- Experience validating models in highly regulated environments.
- Experience mentoring junior validators or leading validation reviews.
- Exposure to inference of rejected transactions and understanding of fraud/credit overlap.
- Familiarity with AI governance frameworks and emerging AI regulatory requirements.
Arbetslivserfarenhet
- Mjukvaruutvecklare (meriterande)
Arbetsgivare
Klarna Bank AB
Sveavägen 46
11134 STOCKHOLM
Ansökan
Ansök senast 30 augusti (2 månader kvar)