I am extremely detail-oriented and highly organized, which allows me to stay on top of competing priorities without letting anything fall through the cracks. I rely heavily on my daily planner to map out my schedule by the hour, assigning specific blocks of time for each task based on deadlines, complexity, and dependencies. I find that I actually thrive when my schedule is full -- it pushes me to stay focused and efficient.

In college, I balanced a full-time finance course load while working a remote internship and babysitting fifteen hours a week. That experience taught me how to quickly assess what needs to be done, break large projects into manageable parts, and stay ahead of deadlines. When everything feels urgent, I evaluate which tasks have the most immediate impact or are time-sensitive (such as meetings or external communications), and then layer in deeper work like financial modeling or deck creation during my peak focus hours.

During my internship after my junior year at a small design firm in Boston, I was brought on to help resolve long-standing accounting issues. The firm was transitioning to QuickBooks, and I was tasked with migrating over half a decade's worth of financial data - every expense and income entry - into the new system. Because this historical data would serve as the foundation for future financial reporting and decision-making, accuracy was absolutely critical.

To ensure the data stayed clean and consistent, I created a detailed migration plan that involved cross-checking entries against bank statements, receipts, and prior internal records. I organized the data chronologically and categorized every transaction according to the new chart of accounts in QuickBooks. Once the historical data was accurately entered, I developed a simple user manual to train every team member going forward.

During my junior year, I worked on a final project for my Finance course that required building a detailed DCF model to estimate the intrinsic value of Delta Airlines. I created a multi-tab Excel workbook that pulled historical financial data to calculate key financial ratios -- such as depreciation-to-sales and operating income-to-sales -- over a five year period. I used these ratios to forecast future financials, which were then fed into the DCF calculation. I also ran sensitivity analyses, adjusting assumptions like tax rates and PP&E-to-sales ratios to assess how valuation responded to different inputs.

In my senior year, for my Equity Analysis and Portfolio Management course, I further honed these skills by building a reusable 'As-Is' financial model for Starbucks using raw data pulled from the Bloomberg terminal. From there, I created three scenario-based model: a Bull case, a Bear case, and a Target model, each reflecting different assumptions on revenue growth, EBITDA margin and long-term growth rates. For example, in my Bull model, I raised growth rates and margins based on optimism around new CEO strategies, while my Bear model accounted for macroeconomic risks like minimum wage hikes in California. This project required managing multiple interdependent sheets, testing assumptions, and reconciling projected share prices against real market data.