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East End Wealth Management adds AI Fellow
December 23,2020

December 23, 2020

 

FOR IMMEDIATE RELEASE

HAMPTON BAYS, NY - EAST END WEALTH MANAGEMENT ADDS DATA SCIENCE FELLOW TO PROVIDE ARTIFICIAL INTELLIGENCE SOLUTIONS FOR FIXED INCOME AND CLOSED END FUND INVESTORS

 

East End Wealth Management (EEWM), a Registered Investment Advisory firm focused on income based solutions, announced today that Remy Canario has joined the firm as a Data Science Associate.  Remy will be focused on strengthening the firm’s process for investing in Closed End Funds and Ginnie Mae government backed securities.

Remy brings his international financial experience to EEWM, having worked at Ballie Gifford in the UK as a Risk Research Fellow identifying investments for the Scottish Mortgage Trust.  His professional projects include Free Lunch, an NLP-based implementation of the Lazy Prices investment strategy by Harvard’s Lauren Cohen, and a Credit Card Fraud Detection project to detect fraud using stacked auto encoders.  Remy holds an MA in Global Risk from Johns Hopkins and a BA in International Studies from Vassar College. 

Steve Kaczmarek, President and Chief Investment Officer of EEWM stated, “We are thrilled to welcome Remy to the East End family.  This addition to our staff will benefit our clients by providing us with AI based approaches to our Closed End Fund investment process.  Also, as we have recently introduced a Ginnie Mae investment option, we look forward to Remy spearheading our efforts in modeling our yield and maturity analysis in that market as well.”

Expounding on his decision to join EEWM, Remy expressed, “I am proud to join EEWM based on the firm’s exemplary client focus and track record of success. As a data scientist and an AI engineer, I intend to utilize my experience and passion for sharpening the investment decision processes with new up to date tools and protocols.”

Further information for media and investment market participants is available by contacting EEWM at 631 594 1069.

                                                                                                                                                                                                                                                                                                                                      &nbs

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