Musbaudeen Ibrahim

I am a certified data scientist with a strong academic background in public health, uniquely positioned at the intersection of advanced analytics, business intelligence, and generative AI. My mission is to leverage data science methodologies and Gen AI capabilities to solve complex problems and drive strategic decision-making across diverse industries, while ensuring my solutions are accessible and usable by domain experts.

With expertise spanning machine learning, causal inference, statistical modeling, business intelligence, generative AI applications, and production model deployment, I specialize in developing predictive and causal models that translate complex data into actionable insights. My latest work extends into uplift modeling and interference detection — applying meta-learner algorithms (T-Learner, S-Learner, X-Learner) to estimate individual-level treatment effects in two-sided marketplaces, achieving a 5.02x ROI improvement through precision targeting. A key focus of my work is deploying machine learning models on platforms like Streamlit, creating intuitive interfaces that enable healthcare professionals, business analysts, and stakeholders to leverage advanced analytics without technical barriers. My portfolio spans causal ML for marketing optimization, healthcare prediction models (heart failure, fetal health outcomes), sales analytics, digital advertising performance analysis, and innovative Gen AI implementations.