E-Payments Risk Data Scientist (Machine Learning)
Sunnyvale, California, United States
USD 104K-160K Entry-level Full Time
Tasks
- Analyze fraud patterns and trends in electronic payments
- Collaborate on fraud detection and prevention product development requirements
- Design test and implement fraud models and strategies
- Develop and optimize fraud and credit risk models
- Execute full modeling lifecycle from data collection to deployment
- Investigate new data sources for predictive value
- Present model performance findings and recommendations
Perks/Benefits
- Employee assistance program
- Employee stock purchase plan tuition reimbursement
- Mental health support
- Paid Holidays
- Paid caregiver leave
- Paid military leave
- Paid parental leave
- Retirement planning
- Time away policies
- Tuition assistance
- Tuition reimbursement
Skills/Tech-stack
Azure | Big Data | Classification | Credit Risk | Credit risk modeling | Data Analysis | Data Quality | Data Visualization | Databricks | Exploratory Data Analysis | Feature Engineering | Fraud Detection | Kafka | Machine Learning | Model Monitoring | Model Validation | PL/SQL | Pandas | Python | Risk Modeling | SQL | Scikit-learn | Spark | Streaming Data | TensorFlow
Education
Associate Degree | Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
Regions
Countries
States
Cities
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