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Hanvitha Muppidi
Entry-level Data Scientist specializing in AML, fraud, and applied machine learning
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About
Data/ML engineer with end-to-end ownership experience at Charles Schwab, spanning data ingestion, anomaly detection, data quality infrastructure, and dashboards used daily by compliance and business teams. Stands out for debugging complex cross-layer issues in systems processing 17M+ records per day and for turning one-off data quality checks into reusable frameworks that scaled across business units.
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Key Strengths
- End-to-end ownership of data and ML systems
- Cross-layer debugging from raw data to reporting
- Built anomaly detection framework processing 17M+ records daily
- Strong root-cause analysis for intermittent model behavior
- Improved internal review workflow with prioritized anomaly queues
- Iterative product improvement based on user feedback
- Created reusable data quality framework across business units
- Scaled onboarding for new business units through abstraction
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