Expert Data Modeler, Fraud Risk Detection

🕒 Setembro 25

🏄 California – Remoto

🏄 California – Remote

💵 $103.669 - $179.693 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🎲 Riscos

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Experian

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1996

💼 Consultoria

📣 Marketing

📦 Logística

Consulting • Marketing • Logistics

A Experian libera o poder dos dados para criar oportunidades para consumidores, empresas e a sociedade.

Descrição

• Investigate large datasets through exploratory analysis and fraud label development • Identify fraud patterns, attack methods, and behavioral signals • Translate fraud and risk problems into hypotheses, analytical plans, model requirements, and measurable success criteria • Develop machine learning models for account opening, account takeover, and identity risk fraud detection • Evaluate models using 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 • Collaborate with teams to deploy 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 covering model behavior, tradeoffs, risks, and recommendations • Follow standards for data privacy, model documentation, explainability, validation, and governance • Report to the Senior Manager of Fraud Analytics

🎯 Requisitos

• 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 • Experience writing modular and tested code for large datasets and distributed or cloud data systems • Experience with pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies • Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration • Experience handling class imbalance, delayed or incomplete labels, changing attack patterns, and model drift • Experience moving models into production, directly or in close partnership with engineering teams

🏖️ Benefícios

• Great compensation package and bonus plan • Medical, dental, and vision benefits • Matching 401K • Flexible work environment with remote, hybrid, or in-office options • Flexible time off including volunteer time off, vacation, sick, and 12 paid holidays • Variable pay opportunity • Comprehensive benefits package • Inclusive and purpose-driven culture