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Fraud Analytics Engineer

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Salary

$103,669 - $179,693

Location

United States — must reside in or near one of our collaboration hubs: Costa Mesa, CA; San Jose, CA; Allen, TX; Austin, TX; Franklin, TN; New York, NY; Schaumburg, IL; Washington, DC; Atlanta, GA; Boston, MA; Seattle, WA

Posted

Today

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

Experience and skills

  • 5+ years of experience in data science, analytics, or a related field
  • Bachelor's degree in Statistics, Applied Mathematics, Econometrics, or another quantitative discipline — or an equivalent combination of education and experience
  • Proficiency in Python—including PySpark, Polars, NumPy, and Pandas—with familiarity in Java
  • Experience with data structures, object-oriented programming, and system design principles
  • Hands-on experience building and maintaining production systems and data pipelines
  • Practical experience with AWS services (e.g., EC2, EMR, Airflow) and cloud security practices
  • Working knowledge of machine learning workflows and analytics model development
  • Experience with CI/CD pipelines, automated deployments, and infrastructure as code
  • Familiarity with containerization and orchestration tools
  • Proficiency in UNIX/Linux environments and scripting (e.g., Bash)

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