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Hinal Kuvadiya
Mid-level Data Analyst specializing in cloud ETL, BI, and machine learning
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About
Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.
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Key Strengths
- End-to-end fraud claims detection using structured + unstructured data
- Applied ML + NLP to reduce false approvals and streamline compliance workflows
- Strong model validation rigor (cross-validation, precision/recall/F1, stakeholder review)
- Practical entity resolution/data deduplication with fuzzy matching and rule-based logic
- Production-grade Python ETL design (modular code, Airflow scheduling, reusable components)
- Scalability optimization for large datasets (batching, optimized pandas operations, monitoring)
- Experience using embeddings/vector DB + fine-tuning to improve search relevance and record linking
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