Osama Yusuf Hassan's Linkedin Analytics

An optimist and an adventurer who has embraced a professional career detour from electrical engineering, seeking to venture into the realm of data science, machine learning, and AI. My enthusiasm lies in working with data to drive action and solve real-life problems. Currently, I am working full-time as a Machine Learning Engineer at The Disrupt Labs on a research and development-based medical project called Non-Invasive Transabdominal Fetal Pulse Oximetry and Its Effect on Reduction of Caesarian Section Deliveries. My core responsibility in this role is to study numerous machine learning models followed by fitting the most appropriate model on a dataset acquired from expecting mothers. The motive behind this is to predict via data-driven decision-making, whether or not a Caesarian Section procedure should be performed on an expecting mother to deliver her baby. In addition to that, I am also involved with the team responsible for pitching the project in front of gynecologists to persuade them into providing requisite raw data for building the desired machine learning model. Also, I regularly attend frequent meetings and participate in day-to-day collaborations with the team of biomedical engineers, medical staff, and other doctors and professionals associated with the project. Hence, this role has inculcated in me a variety of soft skills needed to be a team player and the desired employee. Currently, I seek to land a new, challenging, and growth-oriented role in a machine learning and data science environment, where I can contribute significantly to improving business through data-driven decisions, solve problems as well as work in any capacity that I am required to. I adamantly believe that leveraging technological disruption to revolutionize the world and make it a better place is the way of the future. Technical Skills: -Programming Languages: Python, C++ -Machine Learning: Regression (Simple Linear Regression, Multiple Linear Regression, Decision Tree Regression, Random Forest Regression), Classification (Logistic Regression), Deep Learning (Simple Neural Network), -Data Science: Data Preprocessing (Scrubbing/Cleansing, Feature Scaling, Encoding Categorical Data, Splitting data into training and test sets). -Libraries: Python (Sci-kit Learn, Pandas, Numpy, Matplotlib, Keras) -Tools: Jupyter Notebook, GitHub. I can be reached via email: [email protected] Looking forward to getting in touch and making meaningful connections.