Xavion Barrs

• Led development of a fixed-income mortgage analytics trading system (FiMats) using .NET Core, Angular, C#, and SQL Server that processes $500M+ annually, improving system reliability and enabling scalable loan processing across millions of records.
• Led full-stack development of trader portal features enabling traders to access and customize offering data, implementing C# .NET Core APIs with an Angular frontend and collaborating with business analysts to define requirements and deliver intuitive data visualization controls.
• Architected and implemented a pre-submission validation system within the CECL microservice, creating 60+ data validation rules that let users identify and fix errors before sending data to the Moody's API, cutting the average approval cycle by 50 minutes and lowering submission iterations.
• Developed a full-stack fixed-income inventory pricer using Python, Flask, and scikit-learn linear regression to automate multi-hour bond pricing calculations, reducing processing time by 90%+ through weighted regression models and time-decayed spread analysis.
• Optimized SQL Server queries and indexing strategies across large-scale datasets, reducing peak reporting latency to under 10 seconds.
• Developed Python-based data pipelines on AWS SageMaker and Redshift that support ML workflows and analytics for fixed-income and loan datasets, enabling faster model training and more timely insight generation.

https://solefulapp.netlify.app/

• Full-stack developer on a youth injury risk early-warning platform built with React, Vite, and FastAPI; designed, built, and deployed to production with Plantiga API integration for real-time athlete movement analysis and baseline-driven statistical scoring.
• Architected an API abstraction layer with live and mock modes plus a scoring engine using z-score deviation detection to enable rapid iteration and validation without external dependencies, accelerating product development and user testing.