I learned about the positive impact of diversity and inclusiveness at an early age. I was born in Philadelphia, spent my early childhood in Enugu, Nigeria, grew up in Israel, and lived a year in Quito, Ecuador.
I have a confession. When I was 12 years old, I played Bohemian Rhapsody by Queen for six straight hours. I was completely taken by the complexity and how different musical genres worked together in perfect, never boring, harmony.
I am a quant guy through-and-through. Before joining Kinneret, I co-founded two start-ups. One was specifically dedicated to originating and refining machine learning genetic algorithms for non-discretionary trading, and the other developed analytical and visualization software for algorithm development.
Passionate attention to details, sound data architecture, full transparency, minimal latency, real understanding of needs, and deep knowledge of financial instruments and investing techniques: these themes, commitments, and knowledge inform our internal technology development efforts.
I seek divergence. When things are too homogenous, the results tend to be wrong. It’s why machine learning algorithms need variance. It’s akin to natural selection; when one models a population, there needs to be enough opportunity for variation so that potentially informative mutations can happen out of the unexpected.
At Kinneret, nothing is off the table. We all talk constantly. Unlike larger, more scattered organizations, we are not too many for a potentially good idea to get lost in the noise.