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Nandini Kalita
Senior Data Scientist / ML Engineer specializing in NLP, anomaly detection, and cloud ML platforms
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About
ML/NLP practitioner who built customer-feedback topic modeling (NMF + TF-IDF) to diagnose chatbot-to-agent handovers and drove product/ops changes that reduced operational costs by 20%. Also developed LSTM-based intent recognition using Word2Vec/GloVe embeddings for semantic linking, and deployed an LSTM autoencoder for fraud anomaly detection that cut false positives by 25% while capturing 15% more fraud in A/B testing.
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Key Strengths
- Built NLP topic modeling pipeline (NMF + TF-IDF) to analyze customer feedback and chatbot handovers
- Drove measurable business impact (20% operational cost reduction) by translating insights into product/ops changes
- Strong model validation practice (topic coherence, manual sampling, domain expert review, stability checks)
- Designed iterative, rules-plus-sampling data cleaning process to preserve semantic meaning
- Improved intent linking/search relevance using embeddings (Word2Vec/GloVe) and cosine similarity
- Delivered production-grade Python workflows with orchestration, testing, logging, and scalable processing (Airflow, PySpark, BigQuery)
- Fraud/anomaly detection experience with LSTM autoencoder; improved outcomes via A/B testing (25% fewer false positives, 15% more fraud captured)
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