Experience

AeroDefense

• United States
• Aviation and Aerospace Component Manufacturing
• 1 - 100 Employee

• Lead Hardware Engineer

  • Jun 2020 - Present



Mercury Systems

• United States
• Defense and Space Manufacturing
• 700 & Above Employee

• Manufacturing Engineer

  • Feb 2018 - Jun 2020

  ▪ Perform root cause and CAE analysis to better inform material selection and manufacturing processes for new and existing products▪ Create and maintain 3D models of components, fixtures, assemblies, etc. using AutoCAD and/or SolidWorks▪ Developing Python software for automating the process of programming various manufacturing machinery including wire-bonders and pick-and-place machines▪ Create and maintain manufacturing plans for radio-frequency microelectronics in accordance with ISO, IPC, and MIL standards. ▪ Utilize in-house machine shop and hand tools to create and modify fixtures, housings, and components as necessary



WSP

• Canada
• Professional Services
• 700 & Above Employee

• Mechanical Building Systems Engineer

  • Dec 2015 - Jan 2018

  ▪ Collaboratively managed a team of nine engineers and drafters responsible for the design of building systems in high-rise residential buildings, indoor recreational facilities, offices, and hotels in accordance with New York City Building Code.▪ Participated in mechanical building system design decisions for new construction projects, from initial schematic design to construction administration and LEED certification.▪ Automated daily drafting processes by developing custom AutoLISP functions.▪ Utilized AutoCAD and Revit to design custom mechanical building systems. ▪ Maintained project schedules by regularly communicating with clients, architects, and contractors at weekly coordination meetings.▪ Rotated between full/part time while in school



Stevens Institute of Technology

• United States
• Higher Education
• 700 & Above Employee

• Computer Science Research Assistant

  • May 2015 - Dec 2015

  ▪ Designed and maintained a database and data pipeline capable of pre-processing text data from over a million emails.▪ Developed a machine learning model in Python capable of classifying email sentiment with over 85% accuracy utilizing Naive Bayes and SVM natural language processing algorithms ▪ Designed and maintained a database and data pipeline capable of pre-processing text data from over a million emails.▪ Developed a machine learning model in Python capable of classifying email sentiment with over 85% accuracy utilizing Naive Bayes and SVM natural language processing algorithms