I'm a Production Scheduler by day and a Graduate Student in Data Science by night. I bridge the gap between enterprise operations and data-driven decision making.
I design and orchestrate mission-critical workflows for large-scale banking operations. With expertise in Control-M, Sterling File Gateway, and enterprise automation tools, I've helped modernize core systems and reduce operational complexity.
As a Graduate Student, I am expanding my toolkit into Data Science. I'm new to this field but genuinely excited to learn all things data science—from foundational concepts to advanced applications. I bring a fast-learning mindset and an eagerness to master this domain.
Timeline: 2-Year Enterprise Migration | Status: Production (April 2026)
A comprehensive core banking system conversion project involving the orchestration of mission-critical workflows and file transmissions across enterprise systems.
Key Achievements:
- Designed and deployed 100+ workflows and interfaces
- Reduced operational jobs from 1,300 to 700 (46% efficiency gain)
- Orchestrated critical integrations: ACH, Fed wires, debit card processors, check processing, statements, and reconciliation
- Technologies: Control-M, Sterling File Gateway, IBM File Agent, Automate
Impact: Enabled seamless transition to new core banking platform with minimal customer disruption
Type: Machine Learning Project
Discovered natural customer groupings within a retail dataset using unsupervised learning techniques.
Dataset: mallcustomers.csv (200 customers)
Features: Gender, Age, Annual Income, Spending Score (1-100)
Objective: Define customer personas for targeted marketing strategies
Techniques Used: K-Means Clustering, PCA, Data Visualization
Type: Data Visualization & Analytics Project
Analyzed workforce data to identify key factors impacting employee retention and satisfaction.
Focus Areas: Pay, Age, Job Role Impact on Retention
Deliverables: Interactive visualizations, trend analysis, and actionable insights
Type: Real-World Dataset Analysis
Dataset: apartments_for_rent.csv (10,000 instances)
Target Variable: Price
Key Features: Latitude, Longitude, Bathrooms, Bedrooms, Square Feet, Pets Allowed, Has Photo
Objective: Identify drivers of rental pricing and market trends
Workload Automation & Enterprise Tools:
- Control-M
- Sterling File Gateway
- IBM File Agent
- Automate
Data Science & Analytics:
- Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
- Machine Learning (Linear Regression, Random Forest, K-Means Clustering, PCA, DBSCAN)
- Data Visualization
- Statistical Analysis
Databases & Data Management:
- SQL (PostgreSQL, MySQL)
- Microsoft Excel (Advanced: Pivot Tables, VLOOKUP, Data Analysis)
- Microsoft Access (Database Design & Management)
Email: Brandon.Riley@eagles.usm.edu
LinkedIn: linkedin.com/in/brandon-riley-3208977b
"Take your hands off of it and Let God Be God." I believe in technical excellence and high-precision systems. I build with order so that I can let God automate, trusting the ultimate plan while the systems perform as designed.