Fraud Analytics Summer Intern (Remote & Paid)

The Fraud Analytics Intern will work under the Data Scientist. You will work on a project focused on developing new interdisciplinary fraud attributes using cloud technologies. This will involve exploring data sources across domains and engineering novel features to enhance fraud detection models.

Responsibilities:

• Integrate and analyze data from credit, identity, and behavioral domains to create new fraud features.
• Apply programming and analytical skills for data processing, feature engineering, and model building in cloud environments.
• Collaborate with data scientists and engineers to assess the effectiveness of newly developed attributes.
• Develop and evaluate predictive models using newly engineered attributes.
• Document methodologies and present findings to support knowledge sharing and strategic plans.

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help redefine lending practices, uncover and prevent fraud, simplify healthcare, create digital marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics, and software. We also assist millions of people in realizing their financial goals and saving time and money.

We operate across a range of markets, from financial services to healthcare, automotive, agrifinance, insurance, and many more industry segments.

We invest in people and new advanced technologies to unlock the power of data and to innovate. We're focused on powering opportunities, which is why the Experian Summer Internship Program gives students across the country the chance to apply their education to real-world challenges through meaningful, hands-on projects. Rooted in our 'People First' philosophy, our interns experience firsthand our commitment to personal and professional development. Proudly named one of the Top 100 Internship Programs three years in a row, we invite you to join us and explore your potential with a team that's invested in your growth.

A FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 25,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.

• Currently enrolled in a minimum of a Bachelor's degree program or above in Data Science, Statistics, Computer Science, or related field
• Return to school in the Fall of 2026 to complete degree program
• Proficient in statistical analysis, predictive modeling, machine learning and working with large datasets
• Proficient in Python, PySpark for data analysis and modeling
• Familiarity with cloud computing services (AWS)
• Knowledge in credit or fraud risk modeling

Benefits/Perks:

• Fully remote
• Volunteer Time Off
• Great compensation
• Flexible work schedule
• Eligible for 401(k) participation in 90 days

At Experian, our people and culture set us apart. We're deeply committed to creating an environment where everyone feels they belong and can excel. From inclusion and authenticity to work/life balance, development, wellness, collaboration, and recognition, we focus on what truly matters. Our people-first approach has earned us global recognition: World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ 2025 in 26 countries, and Glassdoor Best Places to Work 2024, among others.

Want to see what life at Experian is really like? Explore Experian Life on social or visit our Careers Site.

Our compensation reflects the cost of labor across several U.S. geographic markets. The hourly 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.

Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

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