Experian is hiring an Analytics Engineer (Remote)

The Fraud Analytics & Commercialization team drives Experian's fraud analytics business through four integrated functions: pre-sales engagement, scalable and custom solutions, consulting, and operational enablement, with the goal of becoming the industry's provider-of-choice. We're looking for an Analytics Engineer, Fraud Analytics Infrastructure, to join our Fraud Analytics team. As the ideal candidate, you thrive at the intersection of data engineering, infrastructure, and machine learning, and care about the reliability, scalability, and usability of the platforms your colleagues depend on. You'll work closely with data scientists, engineers, and product partners to build, maintain, and continuously improve the analytics ecosystem that enables fraud attribute development, model building, and model deployment, including identifying opportunities to increase efficiency and stability across the full modeling lifecycle. Core skills include navigating ambiguity, an impact-focused mindset, critical thinking, and an eagerness to collaborate across teams. You are curious about the latest tools and AI solutions, will evaluate their potential, and help bring the best of them into the team's workflows. Candidates who have taken an unconventional path and demonstrated the curiosity to figure things out without a blueprint will find this role and team to be a good fit. You will report to the Data Modeling Director. You'll have the opportunity to: - Build scalable Python-based data pipelines and backend services for analytics workflows. - Design software systems using object-oriented programming and sound engineering practices. - Create and support platforms that allow analytics development, model training, and model deployment. - Implement and maintain CI/CD pipelines and infrastructure-as-code solutions for automated deployments. - Manage cloud and on-premises analytics environments, including AWS infrastructure and security controls. - Monitor, troubleshoot, and improve data pipelines, platform performance, and system reliability. - Support machine learning and fraud modeling workflows, including feature engineering and model deployment. - Implement new technologies, including AI-based solutions, to improve platform efficiency and stability.