Business operations with technical build ability.
Business + Tech + Execution — turning ideas into things that actually work.
Business
Operations proof for teams that need builders.
Operations-minded, technically curious, and comfortable in environments where details, teamwork, and execution matter. I connect planning with practical build work: real jobs, real coursework, and projects that move from idea to delivery.
Ground ops, event execution, data work, and web projects.
Internships, roles, and serious project work.
Education
CU BoulderB.S. Business Administration, Operations Management, with computer science integration.
Availability
Selective projectsInterested in opportunities where execution quality matters more than noise.
Best fit
Hands-on teamsOperations, logistics, technical builds, and fast environments with real deliverables.
Experience
Work that proves reliability under pressure.
De-icing Technician, Aeromag
Aircraft ground operations, safety procedure, and winter-response execution in time-sensitive conditions.
Event Technician / Stagehand, Rhino Colorado
Setup, teardown, and team execution for live events with changing timelines and hard deadlines.
Community Assistant, Residence Life
Frontline student support, consistent communication, and steady decision-making in a high-trust role.
Projects
Coursework and build work with signal.
- askBelynda: market research and startup audience targeting work.
- Leeds BASE Simulation: team operations and strategy work.
- C++ Board Game: object-oriented programming project focused on clean implementation.
Featured class project
CSCI-4502 Data Mining Class Project
Built for a data mining class, this project studies how food macronutrients relate to food prices over time using USDA FoodData Central, USDA ERS Food-at-Home Monthly Area Prices, and BLS retail food price CSVs. The work combines exploratory analysis, feature engineering, normalized price comparisons, and PyTorch-based prediction inside a script-driven Python codebase.
- Cleaned and merged multi-source nutrition and price data, mapped food and nutrient IDs into readable groups, and visualized nutrient distributions, category averages, time-series shifts, nutrient density, and seasonal patterns.
- Converted food prices into price-per-1000-calories comparisons and graphed macro-oriented foods with raw, min-max normalized, and z-score normalized views to compare stability and volatility over time.
- Used ML algorithms in PyTorch, including feedforward neural-network regression models, to impute or predict missing monthly 2025 prices for foods such as beef, bacon, cheese, chicken, flour, ham, and steak.
Python
pandas
PyTorch
ML algorithms
matplotlib
seaborn
USDA + BLS CSVs
Working Snapshot
Quick details.
FocusBusiness operations, systems, and digital products
ProgramsOperations Management + CS Integration
SkillsPython, pandas, PyTorch, C++, Excel, logistics
StyleReliable, practical, execution-first
How I Work
Clear systems over unnecessary complexity.
I tend to do best in environments where someone needs structure, follow-through, and a person who can keep moving once the work gets real.
This version of the site keeps the public-facing material high level and lets the work samples carry more of the signal.