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Srikanth Nagesh
Junior Data Analyst specializing in machine learning and institutional research
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Data/automation engineer with experience turning manual institutional reporting into production self-service analytics at BGSU, cutting turnaround time by 30% and eliminating semesterly reconciliation work. Also brings PySpark-based legacy data normalization experience at Hexaware and has built RAG-style LLM workflows with evaluation metrics and human-review guardrails.
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Key Strengths
- Owned end-to-end reporting automation from manual Excel workflow to production dashboards
- Improved reporting turnaround time by 30%
- Eliminated manual reconciliation work for analysts
- Built stakeholder trust through parallel-run validation against manual process
- Production-grade Python pipeline design with testing, logging, and failure alerts
- Strong data quality and validation focus
- Legacy data normalization across inconsistent APIs and flat files using PySpark
- Config-driven architecture that scaled across departments without core code changes
- Effective scoping with non-technical stakeholders using time-saved business cases
- Applied guardrails and human review in LLM/RAG workflow design
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