Fraud ML Validation Engineer: Model Governance & AI Tools

PLP Group; Stockholms kommun; On-site; SEK 900,000 - 1,300,000; Full time

Job summary

PLP Group is seeking a highly skilled fraud validation expert to independently validate end-to-end ML models used for fraud detection. You will review methodologies, implement challenger analyses, and build agentic AI tools to streamline validation and surface risks.

The role requires strong Python/SQL, experience with large-scale datasets, and familiarity with cloud platforms and CI/CD practices. You will collaborate with data scientists and stakeholders to ensure transparent risk communication.

Qualifications

• Advanced degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering.
• 3+ years of hands-on fraud-related modeling experience.
• Strong expertise in ML methods for fraud detection, including tree-based models, anomaly detection, graphs, and GenAI.
• End-to-end ML lifecycle expertise from design to production monitoring.

Responsibilities

• Perform independent end-to-end validation of fraud detection ML models and develop challenger models.
• Review methodologies, assumptions, and implementation choices (scikit-learn, LightGBM, graph models, anomaly detection).
• Build and deploy agentic AI tools to automate review of model docs and code.
• Assess model performance with fraud metrics (precision/recall, ROC-AUC, PR-AUC) and business impact.
• Evaluate stability, drift, retraining, and production monitoring practices.

Skills

Python

SQL

PySpark

LightGBM

Graph models

GenAI

Cloud AWS

Docker

Jenkins

Education

Master’s or PhD in a quantitative field

Tools

scikit-learn

SageMaker

S3

Athena

Job description

PLP Group is seeking a highly skilled fraud validation expert to independently validate end-to-end ML models used for fraud detection. You will review methodologies, implement challenger analyses, and build agentic AI tools to streamline validation and surface risks.

The role requires strong Python/SQL, experience with large-scale datasets, and familiarity with cloud platforms and CI/CD practices. You will collaborate with data scientists and stakeholders to ensure transparent risk communication.