Team Liftouts for Big Data and AI in Investment Management – Opportunities and Challenges

As with investment management and distribution, liftouts are a preferred route to get an experienced AI/ML team that works well together to hit the road running – no matter whether it’s an investment product with a track record, a distribution/sales team or a group of data, machine learning or AI scientists.

Ideally, these teams have strong leaders and an average of 10 years working together. For AI and data science, the nucleus of the group can be smaller, since some of them often are tinkering in university labs or with startups in a part-time advisory capacity, but they all are ready to jump back into the boat together for the right opportunity.

We have focused on liftouts and boutique M&A since 1969 (50 years in March 2019), and the newer push into big data, predictive analytics and AI/ML is no different – however, with AI it is absolutely crucial to understand the skill sets, the enterprise-wide opportunities and how to incentivize and compensate the leaders and the lower-level data scientists appropriately. Access data reference points, incentive schemes, salary ranges across experience levels and industries here.

A couple of examples:

Stevie Cohen when starting P72.vc lifted out the leadership team from the CIA’s strategic investment arm, In-Q-Tel, and jumpstarted his deep dive into AI, both as an investment class and global theme. Access the full case study here.

 

UBS when pushing into big data and how to use it globally for research five years ago, lifted out the Morgan Stanley investment research/data scientist team to set up the UBS Evidence Lab/UBS Neo, alongside a range of other global data initiatives. Access the full case study here.

 

Barclays in March 2018 took the principal data scientist from BuzzFeed along with a group of people – he also is adjunct professor at Columbia University, de facto offering two groups of people to join him. Access the full case study here.

 

RenTec of course was one of the pioneering AI liftout success stories, when they took about ten star IBM scientists in a movie script worthy liftout in the 1990s, including string theorists Peter Brown and Robert Mercer. “What do computational linguists know about overseeing investments?” Turns out, quite a bit. Access the full case study here.

 

Uber lifted out a team of 50 from Carnegie Mellon, gutting their robotics department a few years back to develop AV across the street. Carnegie Mellon by now is somewhat used to it, since their leading minds get poached by large institutions all the time (JP Morgan et al). Access the full case study here.

 

Amazon poached the flying drone research team from the University of Sheffield to lead the firm’s Cambridge Development effort. Not a new development for Amazon, which in the pre-Alexa days had to resort to M&A on a big scale since nobody wanted to work for them at that point. Access the full case study here

 

When Samsung in 2018 opened AI labs in Canada, the UK, the US and Russia – Korea opened in November 2017, with a targeted headcount of 1,000 AI researchers by 2020, they went on a team liftout spree, with high profile teams from Microsoft, Google and others. Access the full case study here

 

Facebook in 2013 poached a high-profile NYU team to set up FAIR (Facebook AI Research) with a blank check to build the best AI Lab in the world – this year has seen some changes, including with additional teams from IBM’s Big Data Group, but the modus operandi stayed the same – surgical team liftout. Access the full case study here

– It can also go the other way around. Last year, Credit Suisse’s Global Head of Macro Investing (31 years with the firm) and the Chief Data Scientist (7 years with the firm) left CS to set up their own AI investment boutique, taking with them the team of ML scientists they had assembled.

90% of investment managers still don’t have any concrete plan how to implement predictive analytics, big data and ML into a broader AI game plan. However, that still leaves dozens of case studies of successful and not so successful endeavors globally in 2018, including key hires, team liftouts and acquisitions, in what is the mad rush for AI talent to be one of the winners in the coming years. Take a look at the M&A lists, team liftouts and senior hires here, along with the available global AI talent from our database.

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Written by

Daniel Enskat

Daniel has written over a dozen books on the global asset management industry and has lectured at universities around the world alongside speakers such as Secretary of State John Kerry, Dr. Mark Mobius, ex-Fed Chairman Alan Greenspan, Professor KC Chan and former Prime Minister Gerhard Schroeder.

He is widely sought after for presentations, discussions and his perspective on the global asset management industry, and in the last two decades has advised hundreds of investment management CEOs on strategy and global expansion.