Senior Data Scientist - Fraud Model Validation

Klarna; Bardi; Ibrido; EUR 65.000 - 95.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work model (2-3 days in office)

Diverse, inclusive culture

Cutting-edge AI projects

Impactful role safeguarding payments

Descrizione del lavoro

Klarna is seeking a second-line role to validate fraud models across payments, logins, and identity. You will reproduce results, stress-test methodologies, and ensure governance and production readiness through the model lifecycle.

You will work with first-line teams to surface risks and maintain model trust at scale, shaping validation tooling and agentic AI workflows. This mission-driven position strengthens fraud defenses at a large, data-driven fintech, offering hybrid/onsite work and

Competenze

• 3+ years hands-on fraud modeling experience.
• Proficiency in Python and SQL; PySpark experience preferred.
• Experience with tree-based models (LightGBM) and anomaly detection.

Mansioni

• Validate fraud models and assess performance with fraud-specific metrics.
• Review large transaction datasets and feature pipelines for leakage/ representativeness.
• Evaluate drift detection, retraining, and production monitoring.
• Assess CI/CD and deployment controls (Docker, Jenkins, AWS) for model environments.
• Evaluate governance docs, explainability, and regulatory compliance.
• Document risks and communicate outcomes to data scientists, ML engineers, and stakeholders.

Conoscenze

Fraud modeling

Python

SQL

PySpark

Spark

LightGBM

Anomaly detection

Graph models

Graph networks

Model risk governance

Explainability

Stakeholder communication

Regulatory compliance

Mentoring

Formazione

Advanced degree in quantitative field

Strumenti

Docker

Jenkins

AWS

Descrizione del lavoro

In this second-line role, you validate fraud models used to protect payments, logins, and identity at Klarna. You’ll reproduce results, stress-test methodologies, and ensure governance and production readiness across the full model lifecycle. You work closely with first-line teams to surface risks and maintain model trust at scale. You’ll shape validation tooling and agentic AI workflows to keep validation pace with rapid development. This is a mission-driven opportunity to strengthen fraud defenses at a large, data-driven fintech.

• competitive compensation
• hybrid/onsite work (2–3 days in office)
• diverse, inclusive culture
• opportunity to work with cutting-edge AI
• impactful role in safeguarding payments
• career growth and cross-team collaboration
• Assess model performance using fraud-specific metrics and balance business trade-offs
• Review large transaction datasets and feature pipelines for representativeness and leakage
• Evaluate drift detection, retraining strategies, and production monitoring
• Assess CI/CD and deployment controls (Docker, Jenkins, AWS) for model environments
• Evaluate governance documentation, explainability, and regulatory compliance
• Validate emerging techniques (graph networks, anomaly detection, GenAI-based systems) and document risks
• Communicate validation outcomes and risks to data scientists, ML engineers, and stakeholders
• 3+ years hands-on fraud modeling
• Fluency in Python and SQL; experience with PySpark or Spark
• Experience with tree-based models (LightGBM), anomaly detection, graph/network models
• Experience across ML lifecycle from feature engineering to deployment and monitoring
• Ability to explain complex models and communicate to non-technical stakeholders
• Knowledge of model risk governance, bias, fairness, and privacy considerations
• Experience building or validating agentic AI workflows
• Bonus: advanced degree in quantitative field; domain experience in BNPL or payment products
• Mentor or lead validation discussions is a plus
• ability to challenge approaches constructively
• detail-oriented with risk awareness
• LightGBM
• anomaly detection