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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