Note: The job is a remote job and is open to candidates in USA. Experian is a global data and technology company operating across fraud analytics and commercialization. The Data Scientist will develop fraud detection models and predictive features, investigate fraud patterns, evaluate model performance, and help deploy solutions into decisioning environments while following data privacy, documentation, explainability, validation, and governance standards.
Responsibilities
- Investigate large datasets, including exploratory analysis and fraud label development, to identify latest fraud patterns, attack methods, and behavioral signals
- Translate ambiguous fraud and risk problems into clear hypotheses, analytical plans, model requirements, and measurable success criteria
- Develop machine learning models for fraud detection across account opening, account takeover, and identity risk
- Evaluate models using metrics like ROC/AUC/KS/Gini, precision/recall, fraud capture rate, false-positive rate, customer friction, and fraud losses prevented
- Develop and validate predictive features using identity, transactional, consumer credit history, device, behavioral, temporal, velocity, network, and third-party data
- Write clean, efficient, well-tested Python and PySpark code, and collaborate with teams to bring models and features into batch, retro, or real-time decisioning environments
- Monitor feature quality, model performance, population changes, and fraud-pattern drift
- Design and present analyses for model behavior, tradeoffs, risks, and recommendations
- Follow appropriate standards for data privacy, model documentation, explainability, validation, and governance
Skills
- * At least 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, engineering, data science, or another quantitative discipline
- * Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models
- * Demonstrated experience creating meaningful fraud features
- * Proficiency in Python and PySpark, with experience writing modular and tested code for large datasets and distributed or cloud data systems
- * Experience using common data science and machine-learning tools such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies
- * Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration
- * Experience navigating challenges common to fraud modeling, including class imbalance, delayed or incomplete labels, changing attack patterns, and model drift
- * Experience moving models beyond experimentation and into production, either directly or in close partnership with engineering teams
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 including volunteer time off, vacation, and sick time
- 12 paid holidays
- Variable pay opportunity
- Comprehensive benefits package
Company Overview
Company H1B Sponsorship