Muhammad Osama Email & Phone Number

Alexandria, Virginia, United States

• Orchestrated end-to-end data science projects, including data collection, extraction, preprocessing, and preparation for AI modeling, ensuring high-quality and reliable data inputs.
• Finetuning modern LLM’s (Falcon, Llama) for specific downstream NLP tasks using instruction-based tuning. Optimizing model for deployment using PEFT (Parameter Efficient Tuning) methods. Utilized prompt engineering.
• Leveraging AWS to develop, build, test, and deploy scalable machine learning pipelines, enabling rapid model iteration and deployment.
• Collaborated closely with cross-functional teams to define project requirements, gather domain insights, and translate business problems into machine learning solutions.
• Communicated complex technical concepts and findings to stakeholders and non-technical teams in a clear and concise manner, fostering effective collaboration and decision-making.

Boston, Massachusetts, United States

• Conducted comprehensive data collection for 60 distinct weight lifting exercises by leveraging web scraping techniques in Python.
• Utilized OpenCV and MediaPipe libraries to perform precise pose detection, enabling the identification and extraction of X, Y, and Z coordinates of body joints.
• Employed advanced feature engineering techniques to transform the collected coordinate data, creating additional informative features that enhanced the overall analysis and modeling process.
• Leveraged machine learning algorithms such as KNN, Decision Tree, Random Forest, and XGBoost to differentiate weightlifting exercises based on the generated data, achieving an impressive accuracy of up to 75%.
• Contributed to the development of a robust machine learning pipeline for exercise classification, enabling accurate exercise recognition and potential applications in performance tracking and personalized training.

• Spearheaded cross-functional collaboration and provided leadership to five teams, consisting of four field service engineers and 12 technicians, ensuring seamless project evaluation, proposal, and implementation.
• Led the analysis of turbine wind speed and production data, leveraging data-driven insights to identify root causes behind lower turbine production efficiency. Through the implementation of targeted improvements.
• Led customer meetings, addressing queries, providing solutions, and presenting turbine productivity reports, while facilitating discussions on efficient resolutions for identified issues.

Master Of Science - Ms, Applied Data Analytics, 3.94/4.00

Courses: Foundations of Analytics with R, Data Analysis & Visualization with R, Data Science with Python, Web Analytics & Mining, Machine.

Bachelor Of Science In Electronic Engineering, Electronics Engineering