It's not every day that you leave the rates desk of a leading investment bank and align yourself to Google, but this is what ex-Goldman Sachs rate trading managing director (MD) David Ha did in late 2016. Eighteen months on, and Ha is now hiring for the technology firm.
For those who need their minds jogged, University of Toronto graduate Ha spent eight years at Goldman Sachs, where he became an MD at the age of 32 and the head of the bank's Tokyo rates desk. However, he quit in 2016 to take up a coveted residency at Google Brain in San Francisco. Now Ha's back in Tokyo with Google - and recruiting for a new team at Brain.
Ha announced the new roles in the tweet we've embedded below. In a related post on Reddit he explained that you don't need to be Japanese to apply. Nor do you necessarily need a PhD - although the research scientist roles currently being advertised at Google Brain in Tokyo specify a PhD as a prerequisite - in future there will be some that won't.
Excited about expanding the Google Brain team in Tokyo. If you are excited to work on fresh and interesting AI research, please consider applying! https://t.co/SCI9AyppSN pic.twitter.com/6tHaHiVeEw
— hardmaru (@hardmaru) April 26, 2018
Research scientists at Google work on areas like machine perception, data mining, machine learning, and natural language understanding, often alongside academics.
Ha himself is now a research scientist at Google Brain, despite not having a PhD of his own. Most people at Google Brain have the higher level qualification, but Ha (who went straight into banking after completing a masters degree in maths and a bachelor's in engineering science) has seemingly managed to circumvent it by producing an impressive series of regenerative neural network models, some of which have drawn their own cats and pigs.
Ha declined to comment for this article.
Previously, one Google Brain resident told us why it makes sense to get into artificial intelligence if you possibly can. “Finance is of miniscule importance in the grand scheme of things,” he said. “There are far more interesting problems that can be solved with machine learning, from healthcare, to drug discovery, automated driving, robotics, and energy,
“The roles of doctors, lawyers, flow traders, middle management, accountants, economists, may be redefined or augmented with machine learning algorithms,” he adds. “A single professional worker in five years may have the productivity output of an entire team at present.”
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