A data science professional with 2.5+ years of experience, specializing in predictive modeling, time series forecasting, and supervised machine learning algorithms. I have a strong grasp of Data Structures & Algorithms and SQL, allowing me to translate complex data into clear and actionable insights. Throughout my career, I have created, developed, tested, and deployed highly adaptive diverse services to translate business and functional qualifications into substantial deliverables.
In my current role as a Deep Learning Data Scientist at GNA Energy, I am responsible for building a machine learning-based model for electricity price forecasting in the Day-Ahead Market. I utilize advanced machine learning techniques to forecast electricity demand, supply, and outage, integrating these predictions as inputs for electricity price forecasting. Additionally, I have experience in Natural Language Processing (NLP) and Large Language Model (LLM).
Prior to this, I worked as a Data Scientist at ReNew Power, where I focused on building a machine learning-based digital software for wind power generation forecasting. I employed multiple ML models for wind speed prediction and utilized an adaptive combiner algorithm to generate the final wind speed output. This resulted in a significant improvement in MAPE and NMAPE compared to initial models.
