Fraud Analytics Engineer

Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.

We're looking for an Analytics Engineer that operates at the intersection of data engineering, infrastructure, and machine learning.

You'll help to develop the analytics ecosystem that enables fraud attribute development, model building, and model deployment . Examples include identifying opportunities to increase efficiency and stability across the full modeling lifecycle. This is an individual contributor position and you will report into the Analytics and Data Science Director.

What you'll do

• Build scalable backend services and data pipelines in Python, transforming large datasets into clean, usable inputs for analytics workflows
• Design and maintain systems using object-oriented programming principles, with a focus on readability, modularity, and long-term maintainability
• Develop and operate platforms that support code execution, data workflows, and the end-to-end analytics modeling lifecycle
• Implement CI/CD pipelines and manage infrastructure as code to promote automated, reliable deployments
• Improve analytics environments across AWS and on-premises systems, applying security and encryption best practices throughout
• Monitor, troubleshoot, and resolve data pipeline issues; guide ongoing improvements to system reliability and performance
• Support model development environments by ensuring feature engineering, model training, evaluation, and deployment workflows
• Handle large-scale data ingestion, transformation, and aggregation, and create data visualizations and summaries that communicate findings
• Help evaluate and incorporate innovations that can enhance the team's capabilities

Department: Analytics
Function: Analyst
Experience Level: Mid-Senior Level