Building my engineering expertise: The journey so far.

Working with the Digital & Platform Services team within the Consumer & Investment Banking (CIB) Line of Business to scale and optimize a J.P. Morgan Markets tool. Focused on enhancing accessibility for millions of customers and streamlining user interactions to improve responsiveness and performance. Responsibilities include upgrading the tools infrastructure to support high-volume demand, implementing user-centric design improvements, and collaborating across teams to ensure seamless integration with existing J.P. Morgan digital platforms.

Worked on the System Fault Detection team to identify and diagnose system-level faults through a combination of simulated and on-road testing. Developed a real-time analytics dashboard that tracked key performance indicators, enabling early detection and analysis of latency issues caused by these faults. Collaborated closely with software and hardware teams to refine fault detection algorithms, improving the systems overall resilience and response time.

Worked on the Movidius VPU team to improve processor testing and develop a comprehensive framework for identifying and diagnosing potential testing issues. This included designing automated test cases to validate VPU performance, implementing logging mechanisms to capture detailed error reports, and collaborating with cross-functional teams to analyze and optimize test coverage.

As the Tech Lead for a business fraternity, led a team to develop a ranking system to evaluate and rank potential recruits. This project involved creating and managing an Airtable database to store candidate data and using Python with Pandas for data analysis. We designed a scoring algorithm to assess applicants based on various criteria, ensuring a fair and objective selection process. Responsibilities included data preprocessing, feature engineering, and optimizing the ranking model for accuracy. The project improved recruitment efficiency, providing the fraternity with a streamlined, data-driven approach to select top candidates.

Awarded a $5,000 scholarship to conduct a research project focused on developing a linear regression model for stock data prediction. This research involved gathering and analyzing historical stock prices, identifying relevant predictors, and designing a predictive model to forecast future price movements. The project included data preprocessing, feature engineering, and model training to improve accuracy. The model was evaluated and refined based on its performance against real market data, with findings aimed at enhancing predictive capabilities in financial markets.