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Sailaja Lokasani
Mid-level Data Engineer specializing in cloud ETL/ELT and healthcare analytics
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About
Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.
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Key Strengths
- Built ML/NLP framework to link fragmented healthcare data across EMR, labs, and claims
- Designed multi-step entity resolution process combining rules + ML + NLP similarity
- Validated mappings with holdout samples, precision/recall, and SME spot checks
- Automated data cleanup pipeline in Snowflake + Airflow, reducing manual cleanup by 40%+
- Improved entity linkage accuracy by ~90% through standardization and matching pipeline
- Applied BioBERT embeddings with Pinecone vector DB to improve semantic matching by ~12–15%
- Productionized prototypes into modular, monitored pipelines with validation, logging, and retries
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