Karthik Kumar Raghumurti Bhat

1) I designed and implemented a job-based course recommendation system for over 10,000 users using Python, TensorFlow, and PyTorch, boosting user engagement by 25%. 2) I created an ETL pipeline to ingest job ads and course data from SAP S/4 HANA and used Named Entity Recognition to extract skills. 3) Utilizing NLP, I built a graph linking job ad skills with course descriptions. In collaboration with data science experts, I developed the recommendation system using advanced machine learning techniques.

1) Developed efficient data models within SAP S/4 HANA, yielding a 50% reduction in processing time and notable enhancements in data accuracy. 2) Utilized SQL procedures to refine current data models, resulting in a 60% improvement in performance and efficiency. 3) Successfully migrated the data models from SAP HANA to C4S, amplifying data processing capabilities and accessibility for machine learning endeavours. This initiative sustainably increased efficiency and improved overall productivity.

1) I developed a robust ETL pipeline using Azure Data Factory, Databricks, and Data Lake on Azure Cloud. 2) I conducted EDA using statistical techniques to understand data trends and feature relationships, followed by feature engineering to enhance machine learning models. 3) I built the "Radar Classifier" using Python and classification algorithms to identify radar signals, and the "Smart Feedback System" using NLP for sentiment analysis. Both tools were deployed in Azure and visualized in Tableau.

1) I implemented and maintained ETL processes to ingest, validate, and transform raw data using Azure DataFactory and Databricks, storing the transformed data in Azure Data Lake. 2) I developed Python tools for fighter jets to enhance sortie analysis and updated radar database modules in C++ for defense platforms. 3) I executed SQL scripts, monitored hardware health in the RWR system, participated in RWR software lifecycles, and maintained documentation for ETL processes and version control.