Senior Data Modeler, Fraud Risk Detection

Experian • Madison, MS

2d • $82,644 - $143,249 • Remote

About The Position

Experian's Fraud Analytics & Commercialization operates across four main functions: client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. We are seeking a motivated Data Scientist to assist in building fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. This role requires strategic thinking, a collaborative spirit, and empathy. You will delve into surprising data signals to understand how insights translate into deployed models. Your responsibilities will include investigating the latest fraud patterns, building features, and training and evaluating machine learning models. You will collaborate with senior data scientists and engineers throughout the process, from problem definition to feature engineering, experimentation, and deployment. As a developing programmer, you will be expected to translate theoretical principles into production-ready solutions. We continuously refine our approach through research and engineering, transforming findings into tools and systems ready for commercialization. This is a remote role reporting to the Sr. Manager of Fraud Analytics.

Requirements

• 1+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field
• Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline
• Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection.
• Proficiency in Python, with the ability to write clean, readable, and well-tested code.
• Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn.
• Investigative mindset and the ability to move from unusual data patterns to testable hypotheses.

Nice To Haves

• Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools.
• Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work.

Responsibilities

• Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals.
• Work with senior data scientists to translate fraud questions into testable hypotheses.
• Help build machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction.
• Develop and validate features using identity, transactional, behavioral, and other available data sources.
• Write clean, well-tested code, and work with engineering to bring models and features into production.
• Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions.
• Help prepare analyses and communicate findings to both technical and nontechnical audiences.
• Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance.

Benefits

• 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

What This Job Offers

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