MBA candidate in Financial & Business Analysis. I turn raw data into decisions — building CRM and revenue-operations systems, analytical models in Python and R, and clear reporting that leadership can act on.
From ecological fieldwork in Nairobi to MBA classrooms and revenue-operations teams in Connecticut, my work keeps returning to one idea: good decisions come from good data.
I'm an MBA candidate at Quinnipiac University's School of Business, concentrating in Financial and Business Analysis. My work sits where analytics, finance, and operations meet — I build the systems that capture data, the models that make sense of it, and the reports that help leaders act on it.
Right now I'm a Lead Analyst on a revenue-operations team, where I've built CRM lead-tracking systems, audited sales pipelines, and turned messy records into Power BI dashboards that expose problems worth real money. Alongside that, I support academic operations at Quinnipiac using Python and R. My toolkit spans Python, R, Power BI, Excel, QuickBooks, and GoHighLevel CRM.
My path began in environmental science, where I learned to gather and validate data in the field. That grounding in rigor still shapes how I work: careful, curious, and focused on doing something useful with the numbers.
A selection of roles that have sharpened my approach to analytics, revenue operations, and project delivery.
Three projects that show how I move from a messy dataset or an open-ended business question to a clear, defensible answer. Full write-ups available on request.
Built a classification pipeline in Python to predict which MBA students land job placements, using records for 215 students. I cleaned the data, removed variables that would leak the outcome, and ran full exploratory analysis — histograms, boxplots, and a correlation heatmap — to find that undergraduate academic performance is one of the strongest early signals for placement.
A full analysis in R for a 280-consultant firm deciding how to spend its training and hiring budget. I wrangled messy real-world data (twelve spellings of four program names, three date formats), then answered three business questions with multiple regression, ANOVA with Tukey HSD, and a Welch's t-test — finding that training measurably lifts performance, adoption is uneven across teams, and, against expectations, the firm's "AI-fluent" hires adopted AI tools less than legacy staff.
An MBA research paper proposing a framework for how global firms survive geopolitical shocks. It connects three familiar tools — PESTEL analysis, risk mapping, and scenario planning — to the "sense, seize, reconfigure" model of dynamic capabilities, then tests the idea against how Apple and Nvidia navigated US–China tensions, tariffs, and semiconductor export controls.
The ideas that shape how I approach analysis, operations, and the work I want to keep building.
A dashboard nobody acts on is just decoration. I try to make every analysis end in a clear recommendation — what to do next, and why the numbers point that way.
One-off fixes don't scale. Whether it's a CRM lead tracker or a reusable R function, I aim to build the thing that keeps working long after I've moved on.
Through certifications in Python, machine learning, and Google AI Essentials, I treat AI as a practical, everyday tool for analysis — not a buzzword to chase.
Download the complete CV for printable detail, or reach out directly.
↓ Download Resume (PDF)I'm actively exploring full-time opportunities in analytics, revenue operations, and financial analysis. If our paths might cross, I'd love to hear from you.