Leading the AI Applications Development team as part of the Data Intelligence discipline within the Applied Research & Technology group. Our team's projects span multiple Collins SBUs, and we are dedicated to delivering high quality results quickly. I also currently serve as an individual contributor on a generative AI initiative, for which I am the lead software architect for the US team. Currently focused on API development and integrations.

Promote software development best practices while building scalable, end-to-end data science pipelines. Some key accomplishments during my tenure as a senior-level software engineer and data scientist:- Established a shared framework for quickly developing and deploying machine learning andSME data driven analytics to monitor component health on 787, A320, and 777 aircraft- Helped lead a system architecture rework which integrated two teams’ codebases, resulting inboth compute and storage cost reductions across the project, and an approximate 6x speedup inoverall pipeline processing time- Developed a tablet application to be used by pilots to help monitor aircraft component health inreal time, as well as deliver prognostic insight around component degradation.- Helped develop a new microservice architecture to enable the modular and repeatabledesign/deployment of on-wing ML-enabled analytics.

Data scientist and R&D/Innovation Engineer, Software Developer, I worked on:- Indoor Geolocation: Investigate current advances in indoor localization in order to propose client-specific solutions. Implementing deep learning techniques in order to create a robust, state-of-the art indoor localization system. Primary technologies: Python, TensorFlow- Postage meter (franking machine) fraud detection: Consulting for the Data Lab at Le Groupe La Poste, developing a machine learning-based targeting system capable of identifying fradulent activity in postage meters, Our goal is to determine which subpopulation of postage meters in France exhibits the most actionable fradulent activity over a specificied time interval. Primary technologies: Python, Dataiku DSS, HiveQL, Impala- PND detection: Consulting for the Data Lab at Le Groupe La Poste, developing a machine learning-based identification system capable of identifying addresses in France for which sent mail is non-distributable. Our goal is to assign a PND probability to any given address based on a variety of explicative factors. Primary technologies: Python, Dataiku DSS, HiveQL, Impala

Research Assistant: See "Projects" section.

For 3 months, I worked as a software developer in the Space Systems group. I was working on a project called eKAADs, an acronym for "Engineering Knowledge Acquisition Architecture Development System." My software projects were written in Java.