When everything feels urgent, I rely on a mix of structure and situational awareness to stay focused and deliver effectively. First, I start with a quick prioritization scan — I look at what’s time-sensitive, what impacts other teams, and what’s tied to key decisions. For example, if a financial model needs updating for an investor meeting, that goes to the top. I also check for any tasks that are blocking others, like scheduling with external partners or submitting data needed for a team deck. Then I break my day into focused blocks, grouping similar tasks — like data work vs. communication — so I’m not constantly switching context. I use tools like Google Calendar and Notion or a simple priority matrix to keep track of tasks, deadlines, and who’s relying on what. I’ve also learned the importance of communicating early. If I see a bottleneck coming, I flag it — whether that’s asking for clarification, suggesting a quick sync, or resetting a timeline with a stakeholder. This keeps things transparent and helps avoid last-minute chaos. Overall, it’s about staying flexible but grounded in clarity: knowing what drives impact, working smart, and not being afraid to ask, “What’s the real priority here?”

In my Business Insights through Data course at Hult, I worked on a group project where we analyzed customer behavior patterns to provide strategic recommendations for a retail business. We were handling a large dataset that included customer transactions, demographics, and product-level sales. Maintaining clean and accurate data was critical because our insights directly impacted the recommendations we were presenting. I took ownership of the data cleaning and structuring process, using Excel to remove duplicates, fix inconsistencies in product naming, and ensure missing values were treated properly. I also created a data validation checklist to maintain consistency across the entire team — especially since we were working on different parts of the analysis simultaneously. To align our findings, I set up a shared dashboard in Google Sheets where we would sync our cleaned datasets and track version updates, so everyone was always working off the latest version. We also used filters and named ranges to prevent accidental overrides. Because of this structure, we were able to generate reliable visualizations in Tableau and present a well-aligned story in our final report. It reinforced how data integrity is the foundation of meaningful insights and team alignment, especially in fast-paced environments.

During my Financial Management course at Hult, I worked on a capstone project that required a comprehensive financial and ESG analysis of a publicly traded company — in my case, UNILEVER. As part of this, I built a multi-sheet spreadsheet system in Excel to organize and analyze five years’ worth of financial statements, stock prices, and macroeconomic indicators. The sheets included income statements, balance sheets, cash flow data, and metrics like growth rates, beta, WACC, and capital structure ratios. I also linked in S&P 500 and treasury data to calculate expected returns using CAPM. To manage the complexity, I used formulas to automate calculations, pivot tables to summarize key metrics, and built dashboards with conditional formatting and trend visualizations. I also used AI tools in Excel to forecast growth based on historical trends. One of the most useful features was a dynamic section that allowed me to project the external financing needs for the upcoming year under different sales growth scenarios — integrating assumptions and instantly updating outputs across the sheet. This project sharpened my ability to track financial performance, assess risk, and draw strategic insights from complex data. It also helped me understand how financial and ESG metrics come together to impact a company’s long-term viability.