Remote Fraud Risk Data Scientist Build Production ML Models

Experian; United States; Teletrabalho; USD 110 000 - 170 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Great compensation

Medical/dental/vision

401K matching

Remote/hybrid/in-office

Flexible time off

Paid holidays

Resumo da oferta

Experian is seeking a Data Scientist to build fraud detection models and features, identifying high-risk activity while minimizing friction for legitimate customers. You will explore signals, collaborate with senior data scientists and engineers, and translate theoretical ideas into production-ready solutions.

This remote role reports to the Sr. Manager of Fraud Analytics and involves feature engineering, model evaluation with ROC/AUC and KS, and ensuring governance and privacy standards while

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

Fraud modeling

Feature engineering

Model evaluation

Production deployment

Python programming

Formação académica

Bachelor's or advanced degree in CS/Statistics/Engineering/Data Science

Ferramentas

Python

PySpark

pandas

NumPy

scikit-learn

XGBoost

TensorFlow

Descrição da oferta de emprego

Experian is seeking a Data Scientist to build fraud detection models and features, identifying high-risk activity while minimizing friction for legitimate customers. You will explore signals, collaborate with senior data scientists and engineers, and translate theoretical ideas into production-ready solutions.

This remote role reports to the Sr. Manager of Fraud Analytics and involves feature engineering, model evaluation with ROC/AUC and KS, and ensuring governance and privacy standards while