Praneeth Tondepu

Developed patient-focused machine learning workflows in Python and SQL to accelerate clinical data preparation and improve confidence in research outputs. Built NLP and LLM-powered applications using PyTorch and Hugging Face with retrieval-augmented generation to support medical text analysis. Improved model validation and reproducibility with scikit-learn and managed experiment tracking and comparisons with MLflow for collaborative clinical reviews.

Integrated workplace sensor data for improved observability and reliability of operational systems, targeting higher uptime for critical infrastructure. Created agentic analytics and governance-focused dashboards to improve cross-functional efficiency and decision-ready data accessibility. Built forecasting pipelines using Databricks and Snowflake, performed experiment evaluation with A/B testing, and delivered KPI dashboards using Power BI, Tableau, and SQL for leadership reporting.

Performed data integration and statistical evaluation to speed up business query response times and improve predictive accuracy for operational decision-making. Prepared client datasets and developed classification models using SQL and scikit-learn, applying hypothesis testing to support model validation. Supported recurring analytics with PySpark processing and produced clearer exploratory summaries for business users during review meetings.