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Tejaswini Waghmare
Senior Data Analytics & Data Science professional specializing in Financial Services
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Worked on large financial analytics datasets combining complaint text, transaction logs, and demographics; built end-to-end NLP/ML pipelines (TF-IDF + Random Forest) and data integration in BigQuery with Tableau reporting, citing ~95–98% accuracy. Also implemented entity resolution with fuzzy matching and semantic linking using BERT sentence-transformer embeddings stored in FAISS, including fine-tuning on labeled pairs to improve search/linking relevance.
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Key Strengths
- Unifying structured and unstructured financial data into a single analytical layer
- Building NLP classification pipelines (TF-IDF + classical ML) with strong reported accuracy (~95–98%)
- Entity resolution using hybrid exact + fuzzy matching with precision/recall validation
- Semantic search and record linking using BERT-based embeddings with FAISS vector database
- Fine-tuning embedding models on labeled pairs to improve relevance and reduce false matches
- Designing production-grade Python workflows with modularity, logging, error handling, orchestration, and tests
- Pragmatic transition from experimentation to production (stability/requirements-driven)
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