Senior Machine Learning Engineer (Fraud)
Tasks
- Build and scale feature pipelines and training datasets
- Collaborate with engineering fraud analytics product and ML platform to define requirements and evaluate tradeoffs
- Develop fraud prediction models for real time transaction decisions
- Improve team model building processes
- Instrument measurement and monitoring for fraud pattern changes
- Integrate models into batch and real time decision systems
- Monitor model and data health and manage retraining workflows
- Prototype modeling ideas and run offline experiments
Perks/Benefits
- Dental coverage
- Employee Stock Purchase Plan (ESPP)
- Flexible spending wallets
- Health coverage
- Remote work option
- Tech spending stipend
- Time off
- Vision coverage
Skills/Tech-stack
Airflow | Apache Spark | Batch Processing | CatBoost | Dask | Data Pipelines | Deep learning | Distributed Computing | Experimentation | Feature Engineering | Feature Pipelines | Graph Data | Kubeflow | LightGBM | MLflow | Machine Learning | Model Deployment | Model Monitoring | PyTorch | Python | Ray | Real Time | Real-time Systems | Tabular Data | Time Systems | XGBoost
Education
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