Senior Data Scientist - Fraud Model Validation

Klarna; Monza; Ibrido; EUR 70.000 - 90.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive compensation

Hybrid/onsite work (2–3 days in office

Diverse, inclusive culture

Opportunity to work with cutting-edge

Impactful role safeguarding payments

Career growth and cross-team coll abor

Descrizione del lavoro

Klarna in Lombardia seeks a fraud model validator in a second-line role to validate models across payments, logins, and identity. You will reproduce results, stress-test methodologies, and ensure governance and production readiness across the full model lifecycle.

You will work closely with first-line teams to surface risks and maintain model trust at scale, shaping validation tooling and agentic AI workflows to keep validation pace with rapid development.

Competenze

• 3+ years hands-on fraud modeling.
• Fluency in Python and SQL.
• Experience with tree-based models (LightGBM) and anomaly detection.
• Experience across ML lifecycle from data prep to deployment.
• Knowledge of model risk governance and privacy.

Mansioni

• Assess fraud 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.
• Mentor or lead validation discussions is a plus.

Conoscenze

Python

SQL

PySpark

LightGBM

Anomaly detection

Graph networks

Docker

Jenkins

AWS

ML lifecycle

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