Summary of Expertise A highly results-oriented Lead Data Scientist and Applied Research Specialist with extensive experience in designing and deploying complex AI, Machine Learning, and quantitative solutions across diverse sectors, including finance, retail, and manufacturing. Holding a background in Physics and Mathematics (B.Sc.), I specialize in leveraging rigorous R&D and academic literature to solve high-value business challenges, focusing on measurable ROI. I possess a granted US Patent (US10721528B1) for a specific predictive audience response system, demonstrating a track record of innovative, patentable solution development. Key Areas of Impact My expertise covers the full lifecycle of data science and AI applications: Quantitative Finance & Risk Modeling: Developed unified GNN/LSTM frameworks for integrated asset selection and dynamic portfolio allocation (achieving superior Sharpe Ratios), and hybrid Deep Learning models to enhance financial risk management (Black-Litterman model). Predictive Systems & IoT: Designed and prototyped sophisticated predictive maintenance (PredM) systems for HVAC and gas pipelines (RUL estimation), real-time traffic monitoring, and advanced computer vision systems for unmonitored swimming pool drowning detection (ResNet-50 CNN in PyTorch). Business Intelligence & Forecasting: Led projects in multivariate demand forecasting (Pharmaceuticals), developing dynamic pricing systems (Vehicle Finance), and enhancing e-commerce search relevancy through advanced retrieval frameworks. Technical Skills I am proficient in the MLOps pipeline, numerical development, and cloud environments, including: Python (PyTorch, TensorFlow, SKLearn), MATLAB, Java, Spark ML, Databricks, and Azure. I hold certifications in MLOps and Deep Neural Networks.…Summary of Expertise A highly results-oriented Lead Data Scientist and Applied Research Specialist with extensive experience in designing and deploying complex AI, Machine Learning, and quantitative solutions across diverse sectors, including finance, retail, and manufacturing. Holding a background in Physics and Mathematics (B.Sc.), I specialize in leveraging rigorous R&D and academic literature to solve high-value business challenges, focusing on measurable ROI. I possess a granted US Patent (US10721528B1) for a specific predictive audience response system, demonstrating a track record of innovative, patentable solution development. Key Areas of Impact My expertise covers the full lifecycle of data science and AI applications: Quantitative Finance & Risk Modeling: Developed unified GNN/LSTM frameworks for integrated asset selection and dynamic portfolio allocation (achieving superior Sharpe Ratios), and hybrid Deep Learning models to enhance financial risk management (Black-Litterman model). Predictive Systems & IoT: Designed and prototyped sophisticated predictive maintenance (PredM) systems for HVAC and gas pipelines (RUL estimation), real-time traffic monitoring, and advanced computer vision systems for unmonitored swimming pool drowning detection (ResNet-50 CNN in PyTorch). Business Intelligence & Forecasting: Led projects in multivariate demand forecasting (Pharmaceuticals), developing dynamic pricing systems (Vehicle Finance), and enhancing e-commerce search relevancy through advanced retrieval frameworks. Technical Skills I am proficient in the MLOps pipeline, numerical development, and cloud environments, including: Python (PyTorch, TensorFlow, SKLearn), MATLAB, Java, Spark ML, Databricks, and Azure. I hold certifications in MLOps and Deep Neural Networks.WWWW…