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Mahaboob Johny
Mid-level Data Analyst specializing in SQL/Python analytics, ETL pipelines, and BI dashboards
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About
Data/AI practitioner who built a production LLM-driven healthcare claims analytics and dashboarding system to reduce avoidable ER visits—processing 1.4M+ claims, flagging 19% as non-emergent, and projecting ~$2.8M in annual savings. Demonstrates strong real-world LLM reliability and performance engineering (grounding, numeric validation, caching, materialized views, quantization) plus orchestration experience with Airflow and Azure Data Factory.
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Key Strengths
- Built and deployed LLM-powered analytics tool on healthcare claims data
- Processed 1.4M+ claims; identified 19% non-emergent ER visits
- Projected ~$2.8M annual savings via improved outreach programs
- Strong LLM reliability practices: grounding, strict prompting, and numeric cross-checking to reduce hallucinations
- Performance optimization at scale: pre-aggregation/materialized views, Redis caching, indexing, and quantized models
- Production data pipeline orchestration with Airflow (DAGs, dependencies, retries, alerts) and Azure Data Factory
- Structured AI workflow design/testing/evaluation (modular steps, guardrails, unit + scenario tests, golden sets, metrics tracking, A/B tests)
- Effective collaboration with non-technical clinical and finance stakeholders; translated needs into dashboards and plain-English insights
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