About me

As a seasoned Data Scientist and Machine Learning Engineer, I specialize in leveraging artificial intelligence to unravel complex patterns and predict future trends. My academic journey includes a Master's in Data Analytics Engineering from Northeastern University, where I honed my skills in machine learning and predictive analytics. I am adept at analyzing vast datasets, developing robust algorithms, and implementing deep learning workflows, with a proven track record of exceeding accuracy targets. My practical experience extends across various domains, particularly in the application of AI and ML to environmental conservation, with a focus on underwater mammal research. I have successfully developed machine learning models for classifying underwater sounds with impressive accuracy and precision, demonstrating my ability to apply data science principles to real-world challenges. I am proficient in programming languages such as Python, R, SQL, and Java, with a deep understanding of big data tools like Hadoop, Apache Spark, and cloud platforms like AWS and GCP. My expertise in natural language processing, statistical modeling, and data visualization complements my proficiency in advanced machine learning frameworks like TensorFlow and Scikit-Learn. My commitment to continuous learning is reflected in my active participation as a Global Data Science Ambassador, where I contribute to organizing and executing data science competitions on a global scale. This leadership role has not only allowed me to network with peers and industry leaders but also positioned me as a thought leader in the data science community. With a Bachelor's degree in Computer Engineering and a Master tier achievement on Kaggle, I bring a blend of theoretical knowledge and practical experience to the table. My collaborative approach and dedication to innovation make me a valuable asset to any team seeking to harness the power of data to drive strategic decision-making and operational excellence. Languages: Python, SQL, Java, R, C++, Rust, Git, Matlab, MySQL, NoSQL, Spark, Unix, bash/shell scripting Applied CS: Data Science, Machine Learning, Deep Learning, Big Data, Full Stack, Computer Engineering, Data Engineering, Design Patterns, Computer Vision, NLP Tools and Frameworks: Docker, Kubernetes, Business Intelligence, Maven, UI, RESTful API, Web Services, Hibernate, GCP, PyTorch, MLOps, DevOps, GitHub, Jira, Confluence, Snowflake, Data Lake, Kafka, Map Reduce, Hadoop, Hive, ETL (Pandas, NumPy, scikit-learn, Keras, PyTorch, TensorFlow, OpenCV, Matplotlib, Seaborn)