• Owned the technical architecture for a $2.48B capital infrastructure initiative, building the predictive machine learning pipelines that forecast long-term passenger demand and revenue generation.

• Built a scalable stochastic simulation engine to process complex longitudinal socio-economic datasets, translating massive amounts of behavioral data into precise forecasting metrics that stress-test core financial assumptions.

• Shipped an interactive analytics platform that allows stakeholders to simulate high-variance scenarios , such as shifting EV adoption rates , empowering them to dynamically optimize CAPEX/OPEX structures.

• Developed the underlying analytical frameworks to audit system bottlenecks, successfully validating a 116% Cost Recovery Ratio and ensuring strict compliance with enterprise-grade fiscal standards.

• Mentored 100+ students in Physics Lab, simplifying complex concepts through data-driven experiments and visual analysis.

• Collaborated with the Physics Instructional Team to validate simulations and design lab procedures that boosted engagement.

• Designed structured feedback processes, helping students improve their analytical skills and lab performance.

• Developed SCADA systems and HMI interfaces for Variable Frequency Drives (VFDs), enhancing real-time monitoring capabilities.

• Optimized PLC programming logic using Ladder and Function Block diagrams, reducing operational latency by 30%.

• Automated test setups that improved production testing efficiency by 25%.

• Documented all electrical workflows and created reusable code modules for future automation tasks.

• Worked closely with cross-functional teams to troubleshoot and debug control systems in a manufacturing setting.

• Developed and fine-tuned an LSTM-based stock price prediction model using the Tata Global dataset (NSE), tackling high-volatility challenges in financial data.

• Conducted EDA with Pandas and Matplotlib, identifying key trends and reducing model error through targeted preprocessing.

• Containerized and deployed the ML pipeline using Docker and Flask, enhancing real-time insights on the company’s analytics platform.

• Collaborated with senior ML engineers to streamline model integration and improve forecast responsiveness for end users.