Pendal is welcoming a machine learning quantitative analyst to its bond, income and defensive strategies boutique.
Itay Feldman has been recruited by Pendal - initially on a one year contract - with the goal of integrating machine learning into the bond, income and defensive strategies investment process.
In his new role, Feldman will use machine learning algorithms to provide a layer of filtering and analysis over the investment manager's current suite of quantitative models.
Feldman is a data scientist and joins Pendal from Advantage Data and has previously worked for Microsoft and the State of Illinois.
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He also has experience as an investment banker with MR Beal & Co and Ramirez & Co.
At MR Beal, Feldman was head of quantitative analysis and financial modelling and specialised in building bond optimisation models, fixed income and swap pricing models.
The income and defensive strategies boutique at Pendal is headed by Vimal Gor, who welcomed Feldman's appointment.
Gor said: "We believe that investment markets are entering a period of secular change and we are constantly looking at ways to improve and evolve our invest process to prepare for this change."
"I believe that it is inevitable that machine learning will be embedded into investment decision making processes, the only questions are when to do it, and how."