Expert Data Scientist, Fraud Risk Detection

🔥 8 minutes ago

🇺🇸 United States – Remote

💵 $103.7k - $179.7k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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Experian

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1996

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Experian is a global leader in digital experience, technology, and transformation. They partner with recognized brands to enhance customer understanding, innovate product strategies, and implement agile technology solutions. With a focus on delivering superior customer experiences through AI, cloud architecture, and project management, Experian helps businesses streamline their operations and achieve their objectives effectively.

📋 Description

• Investigate large datasets through exploratory analysis and fraud label development • Identify fraud patterns, attack methods, and behavioral signals • Translate ambiguous 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 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 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 • Work with senior data scientists and engineers from problem definition through feature engineering, experimentation, and deployment

🎯 Requirements

• 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 with class imbalance, delayed or incomplete labels, changing attack patterns, and model drift • Experience moving models beyond experimentation into production • Compliance with applicable work authorization and employment requirements in the United States

🏖️ Benefits

• Great compensation package and bonus plan • Core benefits including medical, dental, vision, and matching 401K • Flexible work environment, ability to work remote, hybrid or in-office • Flexible time off including volunteer time off, vacation, sick and 12-paid holidays • Comprehensive benefits package • Variable pay opportunity • Inclusive and purpose-driven culture • Accommodation support for disabilities or special needs