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About the Role
First-line fraud teams at Klarna build models against real-time attacks on payments, logins, and identity. This role involves independently reproducing results, building challenger models, and stress-testing assumptions from data pipeline to production deployment. It is a second-line position reviewing methodologies built with scikit-learn, LightGBM, graph models, anomaly detection, and GenAI-based components. You will also build tooling, such as agentic AI systems, to automate risk surfacing and ensure validation keeps pace with development.
Responsibilities
- Assess model performance using fraud-specific metrics and weigh each against its real business trade-off.
- Review transaction datasets exceeding 100 million records and feature pipelines with hundreds of features for representativeness, leakage risk, and bias.
- Evaluate drift detection, retraining strategies, and production monitoring practices to confirm they catch degradation before it costs the business.
- Assess CI/CD and deployment controls in Docker, Jenkins, and AWS SageMaker, S3, Athena, and Lambda environments.
- Evaluate model governance documentation, explainability approaches, and compliance with regulatory expectations on model risk, fairness, and data privacy.
- Validate emerging techniques as first-line teams adopt them, including graph networks, behavioral biometrics, anomaly detection, and GenAI-based systems.
- Document validation outcomes and communicate model risks directly to first-line data scientists, ML engineers, and business stakeholders.
Requirements
- 3+ years hands-on experience in fraud-related modeling (transaction fraud, account takeover, identity fraud, or payments fraud).
- Proficiency in challenging implementations of tree-based models (e.g., LightGBM), anomaly detection techniques, and graph or network models.
- Experience across the full ML lifecycle, from feature engineering through production deployment and monitoring.
- Fluency in Python and SQL.
- Experience using PySpark or Spark to process data at scale.
- Experience building agentic AI workflows, designing automation rather than just using off-the-shelf tools.
- Understanding of model validation principles and model risk governance, including assessing bias, fairness, explainability, and privacy risk.
- Ability to deconstruct complex models, explain flaws, and communicate findings to both technical teams and non-technical senior stakeholders.
Skills
- scikit-learn
- LightGBM
- graph models
- anomaly detection
- GenAI
- Docker
- Jenkins
- AWS
- Python
- SQL
- PySpark
- Spark
- agentic AI
Location
- 26 countries
Work Type
- Co-located teams
- Office 2-3 days per week
Experience Level
- 3+ years
Education Level
- Advanced degree (Master's or PhD) in a quantitative field (data science, statistics, mathematics, computer science, physics, or engineering) - Bonus
About the Company
- Klarna is building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances.
- Working here means taking on problems most companies never get to solve and being hands-on with technology.
- Non-obvious backgrounds are welcome.
- Diversity of skills, perspectives, and backgrounds is how we create, innovate, and disrupt.