Job DescriptionThe 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.
Qualifications- 3+ years of experience in data science, analytics, data engineering, or a related field.
- Bachelor's or advanced degree in Statistics, Applied Mathematics, Econometrics, or another quantitative field; equivalent experience considered.
- Experience developing applications and data pipelines using Python, including PySpark, Polars, NumPy, and Pandas.
- Familiarity with Java and object-oriented programming concepts.
- Experience building, deploying, and supporting production systems and data platforms.
- Experience with AWS services such as EC2, EMR, and Airflow, including cloud security best practices.
- Experience with machine learning workflows and analytics model development environments.
- Experience with CI/CD processes, Infrastructure as Code, containerization tools, and UNIX/Linux environments.
Additional InformationBenefits/Perks:- Great compensation package and bonus plan.
- Core benefits including medical, dental, vision, and matching 401K.
- Flexible work environment, ability to work remote, hybrid or in-office.
- Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.
- Explore all our exciting benefits here: https://myexperianbenefits.com/.
Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay range for this position is listed above. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be also eligible for a variable pay opportunity.
#LI-Remote
This is a remote position.
