TREIZEAN (TREY) HALL

Founder • Charlotte, North Carolina

 

I connect data science with everyday business decisions. My experience spans insurance reporting, workflow analysis, and multi-location retail operations.

• B.S. in Data Science, UNC Charlotte • December 2024.

• Chancellor’s List • Spring 2024.

• Tools: Excel, Python, SQL, pandas, NumPy, Power BI, and Tableau.

 

01 / PROFESSIONAL DATA EXPERIENCE

 

Insurance reporting & KPI logic

• Business question: What calculations sit behind advisor production targets?

• Data work: Reverse-engineered and documented metric components and calculation logic.

• Output: An internal production tracker used in day-to-day Brightway Insurance operations.

• Practical focus: Clear daily targets and transparent KPI definitions.

 

Retail operations & performance review

• Context: Managed 13 Paradies Lagardère retail locations at Charlotte Douglas International Airport and oversaw more than 30 employees.

• Information handled: Sales and operating metrics, staffing, scheduling, payroll, and inventory.

• Application: Used operational information to support day-to-day management decisions.

 

Insurance systems & workflow

• Worked with AMS and CRM records, account servicing, and operational workflows.

• Connected record handling with reporting needs and process questions.

 

02 / TECHNICAL DATA PROJECTS

 

Insurance email classification

• Input: Operational email text organized into 16 categories.

• Methods: Text preparation, TF-IDF feature extraction, and LinearSVC model evaluation using Python and Scikit-learn.

• Built: A supervised classification model.

• Designed: A FastAPI, Docker, and Microsoft Dataverse architecture to explore case-routing support.

• Scope: Technical project and architecture design.

 

SmartSummary API

• Input: Text submitted for summarization.

• Built: A Flask API using an OpenAI model, deployed on AWS EC2.

• Engineering: Logging, error handling, environment configuration, and Postman testing.

• Workflow: Git for version control.

 

03 / ACADEMIC DATA ANALYSIS

 

Age & crime victimization

• Context: Led a team project using CMPD datasets from 2011–2016.

• Data preparation: Cleaned and consolidated incident, victim, weapon, and household records.

• Analysis: Binned age groups and evaluated cross-validated models.

• Tools: pandas, NumPy, ArcGIS, Matplotlib, Seaborn, and Tableau.

• Output: A dashboard and public-safety recommendations from the academic analysis.

 

Additional academic work

• Trafficking-location analysis: Data cleaning, feature engineering, and model comparisons.

• Computer vision: A CCTV dataset project and YOLOv8 prototype.

 

HOW THIS SHAPES CLIENT WORK

Start with your questions and the information you already use. Define clear KPIs, validate calculations, and choose a practical refresh process.

 

If records are missing, establish a way to collect real information going forward. Compatibility, data quality, and access determine the scope.

 

Experience and project examples describe my background. Named employers are not presented as Overclocked Ops clients or endorsements. Academic and prototype work does not imply production deployment or demonstrated client outcomes.