Alekya Battu

About

Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.

Experience

Education

Key Strengths

• Built end-to-end ML/NLP forecasting platform for financial trends and market sentiment
• Integrated time-series models (ARIMA/LSTM/XGBoost) with transformer NLP (BERT/RoBERTa) over multi-source data
• Implemented automated model/data drift monitoring with MLflow and Airflow-triggered retraining
• Standardized deployments with Docker and CI/CD across environments
• Designed entity resolution approach combining fuzzy matching + domain rules; validated with precision/recall via test harness
• Improved domain NLP performance via fine-tuning; reported 22% precision lift for sentiment/topic tasks
• Production-grade Python data workflows with modular design, logging, retries, and scalable processing (Spark/Dask, incremental loads)
• Strong stakeholder communication using regular updates and dashboards to drive prototype-to-production decisions