On the ever growing success of our Insight Into events, we turn the spot light away from some of the global organisations we deal with and focus on start-up companies that are disrupting the industry. Will Jackson our resident Data specialist caught up with Peter Bebbington the CTO of Brainpool, a fast growing network of AI and machine learning experts. Will spoke with Peter about his journey to where he is today, and what he sees as the future of AI, Machine Learning, and the growing demand for data scientists.

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Hi Peter, thanks for talking to me today, first of all I just wanted to get a bit of background on you and how you got to where you are today?

I graduated with an MPhys. (hons) from the University of Manchester in 2009, during which time my academic work included projects on modelling the heartbeat of mice.  After this I had a two- year hiatus from academia. In this time, I worked as a software engineer in supply chain and warehouse management before freelancing my programming skills. This experience was my introduction to big data, in a commercial sense, working with retailers to optimise their warehouse automation.

My first introduction to Machine Learning arrived when I returned to academia when I enrolled at King’s College London to undertake an MSc. (hons) in Complex Systems modelling, where I finished top of my class.  After this I enrolled on the ESPRC funded MRes. in financial computing, a programme managed by Professor Philip Trevelean. After completing the MRes. I enrolled on a Ph.D in Physics and Astronomy under the supervision of Professor Ian J. Ford.  My Ph.D included sections on the order statistics of the implied odds as quoted in horse racing gambling markets, information based finance and a reworking of Markowitz portfolio optimization model from a time series perspective.

After your PhD what made you decide to work at Brainpool?

Once I’d completed my thesis at UCL I worked for a hedge fund where I learned about the different strategies that the industry uses to create positive returns.  Paula the CEO at Brainpool approached me, and once I saw the potential and the size of the talent gap in the market for data scientists I agreed to join the team.  AI & Machine learning are going to become part everyday life, for all of us, and thus there is a huge commercial opportunity.

What does your current role at Brainpool involve?

At Brainpool we are a small start-up, so we all do each others’ work at times.  I am the CTO, hence I am responsible for anything tech related and how new technologies can be integrated into the business. Currently I am building the platform for Brainpool with the aim of completely automating the recruitment process.  Currently my work isn’t just coding all day, I pitch to clients and network for the business, which appeals to me because I like talking to people and seeing what other people are doing.

Ok, so how does Brainpool differ from the competitors?

Paula (the CEO) and myself are both academics with a huge number of ties to academia, and what we’re trying to do is bridge a gap between academic and commercial systems.  This is something that’s underutilised in the UK, whereas in America this is done much more widely.  Our main connection is with UCL, through which we connect with a number of world renowned universities such as MIT, Stanford & Berkeley.  This makes us stand out because these institutions are very prestigious.

Competitors also don’t have the same success because they go to networking events and try and recruit Data Scientists; whereas we go to academic networking events, giving talks at conferences and universities to talk to students and academics with machine learning skills.  This makes Brainpool stand out as we understand both the academic system and the commercial side – as we try to act as a bridge between the two systems.

 From both the client or data scientist side we are appealing because we offer a lot of flexibility, depending on the project. We can offer a data scientist for half a day, a day, a week, or 6 months – whatever the client requires.  Data scientists like this because it means they’re not fixed by full time employment contracts, they can instead be ‘digital nomads’.  These are the things that make us stand out from the crowd.