At a Glance
- Tasks: Validate and stress-test fraud ML models to ensure reliability before deployment.
- Company: Join Klarna, a leader in managing fraud risk across high-volume transactions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make a real impact by shaping trustworthy models that evolve with business needs.
- Qualifications: 3+ years in fraud modeling, proficiency in Python and SQL, and strong analytical skills.
The predicted salary is between 59400 - 72600 £ per year.
In this second-line role, you validate and stress-test fraud ML models to ensure reliability before deployment at scale. You’ll work closely with first-line teams to challenge methodologies, reproduce results, and surface risks across data, features, and production pipelines. Your work helps Klarna manage fraud risk across high-volume transactions, leveraging diverse models and emerging techniques. You’ll shape validation tooling and governance to keep models trustworthy as they evolve with business needs.
Responsibilities
- Assess model performance using fraud-specific metrics and business trade-offs
- Review large transaction datasets and feature pipelines for representativeness and leakage risk
- Evaluate drift detection, retraining strategies, and production monitoring practices
- Assess CI/CD and deployment controls across Docker, Jenkins, and AWS environments
- Review model governance, explainability, and compliance with regulatory expectations on model risk and privacy
- Validate emerging techniques (graph networks, anomaly detection, GenAI-based systems) and document validation outcomes
- Communicate model risks to data scientists, ML engineers, and business stakeholders
Key requirements
- 3+ years hands-on fraud-related modeling
- Proficiency in Python and SQL; experience with PySpark/Spark
- Strong knowledge of tree-based models (e.g., LightGBM) and anomaly detection
- Experience with full ML lifecycle: feature engineering, deployment, monitoring
- Understanding of model risk governance, bias, fairness, and privacy risk
- Ability to explain complex models to technical and non-technical audiences
- Experience with agentic AI workflows and automation
- Clear communication
- Analytical thinking
- Stakeholder management
- LightGBM
- scikit-learn
- Graph or network models
Senior Data Scientist - Fraud Model Validation employer: Klarna
At Klarna, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to tackle unique challenges in the fintech space. With opportunities for professional growth and collaboration across diverse teams, our London office offers a vibrant environment where innovation thrives. Join us to make a meaningful impact while enjoying competitive salaries and a commitment to work-life balance.
