Led by curiosity. Backed by data. Designed for impact.
Projects
A Bit About Me :)
I just graduated from Northeastern University with a degree in Finance and a minor in Data Science. With experience in strategic finance, FP&A, and product operations, I’ve become confident in budgeting, forecasting, and turning data into decisions.
I love asking the right questions, building things with purpose, and exploring how data, strategy, and creativity come together to solve problems.
When I’m not working, I’m probably surfing, watching Formula 1, or traveling the world!
Public company fundamentals like earnings per share and revenue are essential for understanding financial performance, but the data is often messy and overwhelming. The goal was to explore this dataset, clean it, and uncover patterns that could help explain relationships between key metrics.
The Approach
I worked with a dataset containing financial fundamentals for 500 public companies, focusing on trends across industries and over time. After cleaning and organizing the data, I used Python and visualization libraries to analyze relationships between variables like revenue and stock price.
Key Highlights
Cleaned and structured financial data for 500 companies
Visualized trends in earnings, revenue, and stock price performance
Compared financial fundamentals across sectors such as Technology and Healthcare
Created visual summaries to support clearer insights
Key Insight
Technology companies showed significantly greater variance in earnings per share year over year compared to other sectors. This pointed to potential risk and reward tradeoffs that became more visible through visual analysis.
What I Learned
This project helped me connect financial concepts to real-world data and improve how I communicate quantitative findings visually. I’m interested in how data analysis can drive better decision-making, especially in finance.
Reversal strategies aim to capture patterns in how poorly or strongly a stock has recently performed. This project used monthly return data to test if past losers become winners and vice versa, helping to spot trends across different portfolio groups.
The Approach
I cleaned and processed a dataset with over 240,000 monthly stock-level returns. I then grouped the stocks into five portfolios based on their prior month performance, calculating and comparing the average returns for each. Finally, I used regression analysis to see how this strategy lined up with common financial factors.
Key Highlights
Created long-only reversal portfolios and measured average monthly returns
Found that the best past performers had significantly higher future returns
Visualized return differences to show how performance varied across groups
Ran regressions against Fama-French factors to understand where returns were coming from
Key Insight
The strongest performing portfolio had the highest future return, while the lowest reversal group underperformed. Regression results showed that most of the return came from exposure to small-cap stocks.
What I Learned
This project helped me understand how to evaluate investing strategies through data. I practiced using financial theory and Python side by side and gained confidence in explaining insights through code, visuals, and stats.
Tools: MySQL, Python, Flask, Spotify API, Ticketmaster API
The Problem
As an EDM fan in Boston, I noticed how hard it was to figure out where to go out. Event info is scattered across apps and platforms, and there’s no easy way to connect your music taste to what’s happening locally.
The Approach
Our team built a database by pulling real-time data from the Spotify and Ticketmaster APIs. We structured it to link artists, events, playlists, and user preferences. Then, we used SQL queries to recommend events and explore venue trends.
Key Highlights
Built a connected database of users, events, artists, and playlists
Used Python scripts to pull and load data from APIs
Wrote SQL queries to recommend events based on Spotify data
Visualized venue activity across the Boston EDM scene
Key Insight
The Middle East venue in Cambridge had by far the most EDM events. We also found strong alignment between user music preferences and event types, showing the value of personalized recommendations.
What I Learned
This project showed how integrating music and event data can make it easier for fans to discover where to go out, and help organizers better understand their audience.
