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
- Deploy model code for production
- Design test and implement fraud models
- Develop and optimize fraud and credit risk models
- Execute full modeling lifecycle
- Investigate new data sources for predictive value
- Monitor model performance in production
- Present model performance findings to stakeholders
Perks/Benefits
- Employee assistance program mental health support
- Employee stock purchase plan
- Paid Holidays
- Paid caregiver leave
- Paid military leave
- Paid parental leave
- Retirement planning
- Training and development
- Tuition assistance
Skills/Tech-stack
Azure | Big Data | Credit Risk | Credit risk modeling | Data Analysis | Data Quality | Data Quality Checks | Data Visualization | Databricks | Exploratory Data Analysis | Feature Engineering | Fraud Detection | Kafka | Machine Learning | Model Deployment | Model Validation | PL/SQL | Pandas | Python | Quality checks | Risk Modeling | SQL | Scikit-learn | Series analysis | Spark | TensorFlow | Time Series | Time Series Analysis
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
Regions
Countries
States
Cities
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