About

Lead engineer for the Default Model Engine (DM Engine), responsible for predicting Probability of Default (PD) and Loss Given Default (LGD) for commercial real estate loans.

• Modernized the engine to meet SOC compliance standards and improve reliability, scalability, and maintainability
• Redesigned the architecture to support multi-source data ingestion via AWS SQS, serverless Redis, and direct invocations
• Built a rapid prototyping framework for scenario testing, accelerating feature development and minimizing risk of regression
• Authored the system's first full documentation set and implemented automated builds using Sphinx and GitHub Actions for continuous deployment and automatic documentation generation
• Collaborated closely with cross-functional teams, including data scientists, product managers, QA, API, and data engineering, to deliver solutions that met cross-team acceptance criteria
• Contributed to frontend modernization by supporting the migration from AngularJS to React, focusing on accessibility, performance, and responsive design

Developed web2 and web3 applications for clients, specializing in NFT tracking systems and machine learning predictions.

• Developed web2 and web3 applications for clients using Node.js, React, and TypeScript
• Aggregated data across different platforms, blockchains, and websites to create a uniform NFT tracking calendar, capable of tracking hundreds of projects daily using Scrapy, Python, and NoSQL databases
• Implemented an alert system to notify users by utilizing change streams in MongoDB to monitor live events and react accordingly
• Designed a Software-as-a-Service (SaaS) that incorporated machine learning algorithms to predict the success of upcoming NFT projects using Python and TensorFlow
• Performed sentiment analysis on social media posts to determine the ratio of bots versus human followers to identify artificial versus organic social media growth using Python and NLP
• Integrated APIs with token-based authentication to allow users to query various endpoints for specific project data using Next.js, GraphQL, and REST APIs
• Provided technical support to customers through Jira and Slack to ensure their needs were fully met

Focused on performance optimization and full-stack development, achieving significant improvements in page load times and user experience.

• Reduced initial bundle size requested by the client, which drastically increased performance for average page load times by more than half
• Decreased average page load time from 3 seconds to under 1 second using Webpack optimization techniques
• Employed code-splitting, compression, and lazy-loading non-critical components to improve overall Google PageSpeed by 45% (from 45 to 90), which significantly improved Search Engine Optimization (SEO) for the website
• Created a Customer Relationship Management (CRM) system that allowed the team to better manage customers across different platforms using Web2py framework
• Designed and implemented PostgreSQL schemas and queries for the backend development team following MVC architecture patterns