[Remote] Senior Data Scientist, Fraud Risk Detection

Note: The job is a remote job and is open to candidates in USA. Experian is hiring a Senior Data Scientist for its Fraud Analytics & Commercialization team. The role focuses on investigating fraud patterns, developing and evaluating machine-learning models and features, and supporting their deployment and monitoring to detect fraud while minimizing friction for legitimate customers.

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

Skills

• 3+ 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
• 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

Benefits

• Bonus plan
• Medical, dental, and vision benefits
• Matching 401K
• Flexible work environment, with the ability to work remote, hybrid, or in-office
• Flexible time off: 20 days
• Volunteer time off
• Vacation
• Sick time
• 12 paid holidays
• Variable pay opportunity

Company Overview

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