About me

An innate problem solver with a background in ECE(VLSI) field, I found my true calling in the realm of Digital Technologies. Recognizing the power of solving real-world problems and meeting business demands, I shifted my focus to Data Science, Artificial Intelligence, and Cloud Computing. I am passionate about inspiring others to grow in these fields. With a strong commitment to discipline and meeting deadlines, I consistently deliver exceptional results while nurturing a continuous learning mindset for personal growth. My expertise lies in Python programming, Data Science, Artificial Intelligence, Machine Learning, and Deep Learning. I am constantly expanding my skills in areas such as Computer Vision, Time Series Forecasting, Natural Language Processing. Additionally, I am keen on gaining proficiency in MLOps leveraging the Cloud Computing platforms such as Microsoft Azure and AWS. Currently, I am thriving as a Data Scientist (with focus on building cutting-edge AI solutions) at Siemens Technology and Services, where I contribute to innovative projects. Previously, I had the opportunity to intern while securing a full-time role as a Systems Engineer at Tata Consultancy Services in the Digital Profile. During my internship, I showcased my problem-solving and teamwork abilities by developing a project called "Video-Bin: AI-Driven Video Search." This initiative aimed to address a significant business problem of huge unstructed video data stored by media businesses without the usability due to lack of annotations/tags. It is not viable for a business to manually tag these video contents, thus our solution provided a AI-powered approach of tagging these videos based on the internal content automatically without human intervention, turning the video data usable. In addition to my professional experience, I have co-authored a research paper titled "Arrhythmic Heartbeat Classification using Ensemble of Random Forest and Support Vector Machine" published in the esteemed IEEE Journal: "Transactions on Artificial Intelligence." Our work introduced a novel approach incorporating niche Feature Extraction Techniques, Feature Selection Algorithms, Data Balancing, and state-of-the-art Machine Learning algorithms, achieving an impressive accuracy of 98.21% on a specific ECG dataset. A long way to go, a lot to learn, working towards that everyday... :) Feel free to reach out for collaboration, networking, or discussing exciting opportunities.