Experience of 9+ years to execute/deploy multiple ML based projects to achieve different purposes. Deploy the production pipeline to detect the multiple causes of a hard drive failure by analyzing the drive logs data which contains enormous features along with noise. Architect the design of an E2E application which can augment users to drive insights from the logs data with complete automation. Have a keen interest in applied mathematics and statistics to find the latent information in structured, semi-structured and unstructured data. Apply Machine Learning Algorithms (Supervised and Un-supervised) and Text analytics techniques like Aspect Based Sentiment Analysis. Have an exposure to automation via cloud computing analytics.

Implement machine learning algorithms in R and Python to predict whether the existed hybrids/possible hybrids can be commercialized in a predefined pipeline based on their raw traits and regression traits using feature engineering. Work on feedbacks from Breeders to improve the performance of the model.

During this period, I have worked upon text analytics and various machine learning algos. Aspect Based Sentiment Algorithms has been developed and deployed for one of the customers. Big data ETL work has been handled via running hive queries over hadoop.Tools used: R, PythonBig data problems have also been tackled with use of libraries like RHadoop, Pydoop