Fraud Analytics Engineer

Experian • Madison, MS

17h • $103,669 - $179,693 • Hybrid

About The Position

Experian is seeking an Analytics Engineer for its Fraud Analytics & Commercialization organization. The role works at the intersection of data engineering, infrastructure, and machine learning, developing the analytics ecosystem that supports fraud attribute development, model building, and model deployment. The position focuses on improving efficiency and stability across the modeling lifecycle. It is an individual-contributor role reporting to the Analytics and Data Science Director.

The role builds scalable backend services and data pipelines, develops and operates platforms for analytics workflows, and supports model development environments. It also contributes to reliable, secure analytics environments across AWS and on-premises systems, while helping evaluate innovations that can enhance the team's capabilities.

This is a hybrid position based in or near one of Experian's collaboration hubs: Costa Mesa or San Jose, California; Allen or Austin, Texas; Franklin, Tennessee; New York, New York; Schaumburg, Illinois; Washington, DC; Atlanta, Georgia; Boston, Massachusetts; or Seattle, Washington. The position is full-time with a day shift. The stated annual salary range is $103,669–$179,693, with variable pay eligibility and a comprehensive benefits package.

Requirements

• At least 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; familiarity with 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 such as EC2, EMR, and Airflow, and with 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, such as Bash.

Responsibilities

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

Benefits

• Great compensation package and bonus plan.
• Medical, dental, and vision benefits.
• Matching 401(k).
• Flexible work environment with the ability to work remotely, hybrid, or in-office.
• Flexible time off, including volunteer time off, vacation, sick time, and 12 paid holidays.
• 20 days of flexible time off.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior