Created a dynamic onboarding and internal tooling platform using React, Next.js, AWS, and Neo4j that centralized company tools, documentation, and workflows into a single interface, streamlining new-hire procedures and boosting team productivity.

Developed a full-stack administrative system for resume parsing and student-job matching, building automated candidate analysis pipelines and scalable APIs that enhanced placement accuracy and supported hundreds of weekly users.

Optimized enterprise web booking experience by implementing backend-driven conditional rendering, streamlining care request flows, and refactoring front-end React components to provide a more seamless user experience, reducing drop-offs and increasing conversions.

Delivered enterprise customizations through PostgreSQL migrations and configuration updates that enabled tailored benefits and web displays for Fortune 500 clients, while instrumenting Amplitude events to measure engagement and guide UX improvements.

Worked through and helped debug, calibrate, and test weekly lab assignments that focus on important concepts such as recursion/iteration, graph search algorithms, and functional programming.

Conducted checkoffs to assess students' understanding of the material and their ability to make well-informed choices for data structures, helper functions, and code readability/complexity.

Achieved a 1.3% increase in visitor-to-premium conversion rates by enhancing the UI/UX of the VHP and Enrollment interfaces, streamlining the navigation experience, and addressing pain points identified through data-driven user testing.

Implemented redesigned UI/UX from user research insights to improve content visibility and navigation, leading to higher engagement (more visitors scrolling through the page) and measurable gains in visitor-to-basic conversions and satisfaction.

Leveraged ML libraries like XGBoost to train predictive models on the dataset, yielding robust insights into the correlation between economic recessions and treatment retention rates among patients with OUDs.