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JPMorgan's massive guide to machine learning jobs in finance

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Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it.


JPMorgan's massive guide to machine learning jobs in finance

#artificialintelligence

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it.


A Definitive Guide to Machine Learning for Finance – sharad jain – Medium

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J.P. Morgan's quantitative investing and derivatives strategy team, led Marko Kolanovic and Rajesh T. Krishnamachari, has just issued the most comprehensive report ever on machine learning in financial services. Titled, 'Big Data and AI Strategies' and sub headed, 'Machine Learning and Alternative Data Approach to Investing', the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don't they'll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release. At 280 pages, the report is too long to cover in detail, but I've pulled out the most salient points and put it into a cool infographic:


BAML hires top machine-learning quant from J.P. Morgan

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Bank of America Merrill Lynch hired Rajesh Krishnamachari, formerly a senior quantitative strategist and researcher at J.P. Morgan, as the head of data science for equities in New York last month. BofA's new equities-focused data-science team is using machine learning and artificial intelligence to get insights from proprietary data and develop new products that have an impact on the top and bottom line of the business. A Bank of America spokeswoman confirmed his employment but declined to comment further. Krishnamachari joined J.P. Morgan's equity derivatives quantitative research team in 2014. Primarily using Python, Java and the XGBoost software library, he designed and back-tested systematic options, VIX and equities trading strategies, as well as an ultra-high-frequency execution algorithm for trading VIX futures.


What Machine Learning Is and How It Can Help Your Business Intagleo

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Championed as solutions to many of our challenges, both old and new, Artificial Intelligence and Machine Learning are hot topics among researchers and industry experts alike. The proponents of these technologies show no signs of going mum anytime soon and their popularity will only soar in the coming years as IDC predicts that worldwide spending on cognitive and Artificial Intelligence systems will reach $77.6 Billion in 2022. While some are already hard at work figuring out ways to capitalize on this technology, for others comprehending what machine learning is and how it can help their business can be quite a head-scratcher. While not exhaustive, this article will attempt to answer some of the most quintessential questions our readers may have about this technology and equip them with the knowledge they need on their path to Machine Learning success. First things first – It can be a little daunting to wrap your head around the specifics of Machine Learning.