“Caron Engineering Announces Four New Team Members”

Caron Engineering, Inc., provider of CNC machine optimization technologies and monitoring solutions, has recently hired four new team members.

 Crystal Chiasson – Office Administrator

Crystal Chiasson is the new Office Administrator at Caron Engineering where she will be responsible for all aspects of the front office management including reception, processing purchase orders and general office support.  Crystal has a diverse background emphasizing in customer service, communication, organization, and business growth.  She comes from over 25 years of business management including most recently admissions and financial aid in post-secondary education.

 Joe Gagnon – Financial Controller

Joe Gagnon is the new Financial Controller at Caron Engineering who will be responsible for accounting, certain HR operations, and establishing, monitoring, and enforcing policies and procedures that affect the financial condition of the company.  Joe graduated from the University of Maine, Orono in 2011 with a BS in Finance and International Business.  He comes to Caron Engineering from a financial lending institution, where he worked as an Assistant Vice President focusing on commercial credit analysis for borrowing customers.

 Lloyd Guerrette – Service Technician

Lloyd Guerrette is the newest Service Technician at Caron Engineering where he is being trained to install and service retrofit products for CNC machines.  Lloyd graduated from the University of Maine, Orono in 2014 with a BS in mechanical engineering technology.   He has previous experience in the oil and gas industry as well as quality control and nondestructive testing of components for directional drilling. He is also an experienced automotive technician and fabricator.

Tyler Romano – Data Scientist

Tyler Romano was a former summer intern at Caron Engineering who graduated with a BS in Physics from the University of New Hampshire in May of 2016.  As the Data Scientist at Caron Engineering, Tyler will be focusing on recognizing patterns and anomalies in machining in order to optimize tool life, predict and prevent failures, and reduce the overall cost of production.