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SUMIT MAMTANI
Mid-level Data Scientist specializing in ML, MLOps, and customer analytics
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ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.
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Key Strengths
- Built end-to-end NLP pipeline to extract and standardize entities from messy insurance claim text
- Improved NER precision by creating a custom entity dictionary and fine-tuning on a manually labeled subset
- Designed and validated an entity resolution pipeline using hybrid deterministic IDs + fuzzy matching
- Quantified linkage quality with precision/recall and manual sampling; cross-checked against historical claim records
- Implemented pragmatic precision/recall tradeoffs via staged matching and empirically tuned similarity thresholds (~0.88) plus business-rule filters
- Applied Sentence-BERT embeddings with a vector database for semantic claim search; fine-tuned with contrastive learning to improve recall (~15%)
- Production-minded data engineering: modular functions, batch parallel ETL, partitioned Parquet, join optimization, containerized scaling on Kubernetes, and automated tests
- Structured experimentation with hypothesis-driven model comparisons (GLM, boosting, ensembles) and traceable experiment tracking
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