Remote Fraud Risk Scientist - ML & Identity Detection

Experian; Northern (KY); Híbrido; USD 104 000 - 180 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Great compensation package and bonus

Medical, dental, vision benefits

Flexible work environment (remote/hyr/

Flexible time off including volunteer

Resumo da oferta

Experian's Fraud Analytics & Commercialization team is seeking a Data Scientist to build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. You will collaborate with senior data scientists and engineers from problem definition to deployment in production environments.

You will investigate fraud patterns, develop predictive features from diverse data sources, write clean Python and PySpark code, and evaluate models using ROC

Qualificações

• At least 3 years of experience in data science, ML, statistics, or related quantitative field.
• Bachelor's or advanced degree in computer science, statistics, engineering, data science or related discipline.
• Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime or adversarial risk models.
• Demonstrated experience creating meaningful fraud features.
• Proficiency in Python and PySpark, with modular, tested code for large datasets and distributed/cloud data systems.
• Experience using pandas, NumPy, scikit-learn, XGBoost, TensorFlow or comparable tools.
• Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and calibration.
• Experience addressing class imbalance, labels delay, evolving attack patterns and model drift.
• Experience moving models from experimentation into production with engineering teams.

Responsabilidades

• Investigate large datasets, perform exploratory analysis, and develop fraud label signals.
• Translate fraud/risk problems into hypotheses, plans, model requirements and success criteria.
• Develop ML models for fraud detection across account opening, takeover, and identity risk.
• Evaluate models using ROC/AUC/KS/Gini, precision/recall, and other relevant metrics.
• Develop and validate predictive features from identity, transactional, credit history, device, behavioral and network data.
• Write clean, efficient Python and PySpark code and collaborate to deploy in batch, retro, or real-time settings.
• Monitor feature quality, model performance, population changes, and drift in fraud patterns.
• Design and present analyses of model behavior, tradeoffs, risks and recommendations.
• Adhere to data privacy, model documentation, explainability, validation and governance standards.

Conhecimentos

Analytical thinking

Fraud domain knowledge

Statistical modeling

Machine learning concepts

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's Fraud Analytics & Commercialization team is seeking a Data Scientist to build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. You will collaborate with senior data scientists and engineers from problem definition to deployment in production environments.

You will investigate fraud patterns, develop predictive features from diverse data sources, write clean Python and PySpark code, and evaluate models using ROC