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Tech Stack
Tools & technologiesCloudNumpyPandasPySparkPythonScikit-LearnTensorflow
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- 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