Experienced Developer with a demonstrated history of bootstrapping solutions for complex problems with limited resources. 6+ years experience with Python scientific and data toolkits such as Pandas, NumPy and Pydantic. 4+ years experience Full Stack Web Development, developing containerized uWSGI backends with Flask and FastAPI and SPA frontends with React/Typescript. I enjoy working with data, transforming it into something useful and presenting it. I strive to create extensible, reusable code and applications.
Develop Url Shortener Web Application with click-through performance metrics visualized with Dashboards for link owners.
Develop Electron Desktop Application, with containerized Flask backend for Web Scraping social profiles. Over 200,000 profiles transformed into structured data and imported into internal ATS.
Driving initiative to design a Skills Ontology to support graph-based Vectorization and Distance Measures of jobs and candidates.
Develop Web Application and Browser Extension to notify users of social profiles tracked in internal ATS. Achieved ~50 ms response times while querying 1 million records.
Develop Containerized Software Applications with Docker. Deploy to AWS using CloudFormation.
Write and Deploy AWS Lambda functions for Serverless microservices.
Design and run Data Pipeline modeled as DAG to match candidates to open jobs. Replaced manual search process with scalable and automated process.
Built internal NLP Pipeline with domain specific Language Normalization and Pattern Detection for job description and candidate profiles.
Develop a company name disambiguation Python Package, allowing accurate reporting of talent flow and attrition.
Adapted and Optimized MATLAB code for Hierarchical NMF into Python Package. Leveraged to provide Unsupervised Clustering for Talent Segmentation.
Military Police (31B)
18 Months Active Duty Deployment
Qualify candidates by conducting phone interviews to ensure technical and cultural match to client needs
Travelled between company properties to supervise and audit property operations
Kivy based application for displaying and managing Markdown notes
Factory pattern provides high-level abstraction of platform specific functionality
Implemented custom layouts and widgets for displaying Markdown
Written primarily in Python with Java utility classes for Android
Adapted research paper MATLAB code to implement hNMF with Python
Scikit-learn compatible
Reduced run-time from hours to seconds
Added several convenience methods for transforming matrices to interpretable latent factors
Fully documented with Sphinx and published with documentation
React Chrome Extension with Authentication
Containerized Flask API with Nginx reverse proxy
Subsecond response times with 1 million records using AWS t3.micro instance
Test suite with pytest and hypothesis
Designed Swagger API and implemented with Flask
React SPA Front-end
Provides powerful reporting capabilities for both links and campaigns
Embedded dashboards and charts
Scraper exists of two main components : Electron Desktop Application and Web Application
Controls Chromium intercepts network requests and parses pattern-matched JSON
Compatible with SPA sites
Parses raw strings into structured data. Transforms data into spreadsheet form compatible with internal ATS.