Mastercard’s Steve Flinter talks about his career to date and imparts wisdom for those who want to succeed in their own career.
Data science leaks into a world of technology and career areas, including AI and machine learning.
But what is it like working within that space? We talked to Steve Flinter, director of artificial intelligence practice at Mastercard Labs, to find out about his career journey and what advice he would give to those coming up the ladder behind him.
What first stirred your interest in a career in this area?
In school, I was always fascinated by technology in general and computers in particular. I recall saving up for my first computer, a Sinclair ZX81, for months with odd-jobs and pocket money. By modern standards, the ZX81 was an extremely primitive machine – 1KB of RAM, an 8-bit processor that ran at 3.25MHz and an external cassette tape for storage. Despite those limitations, it was a great little computer to learn on, and gave me my entry into the world of computing.
Later on, while studying computer science, I became really interested in the whole area of artificial intelligence – how one could program computers to perform tasks that would otherwise require human intelligence to complete.
What education and other jobs led you to the role you now have?
In terms of education, I completed a BSc degree in computer applications in Dublin City University and, after graduation, completed a PhD in artificial intelligence in Trinity College Dublin under Prof Mark Keane (now with University College Dublin).
After I graduated with my PhD, rather than pursuing an academic career, my interests lay in commercial software development. For the first 10 or so years of my career, I worked in a variety of tech roles, mostly with small, indigenous software companies, initially as a programmer and then working my way up to being an architect and ultimately a CTO of a small company.
I then was offered a really interesting opportunity to join Science Foundation Ireland (SFI) in 2005. SFI was a very new organisation at the time – still in start-up mode – and they were looking for someone with a background in software and computer science to manage the portfolio of projects in those areas. I stayed with SFI for almost nine years, working on a variety of different roles, the last of which was to develop and run the SFI research centres programme, which ultimately led to the establishment of the first major SFI centres, such as Insight and AMBER.
Then, in 2014, I had an opportunity to move back into the private sector with Mastercard, helping to set up their start-up engagement activities through a team called Mastercard Start Path. I spent about four years in that role until earlier this year, when I had the opportunity to set up the artificial intelligence practice within the Mastercard Labs R&D group. In many ways, this role feels like my career coming full circle, back to the area of computer science that so fascinated me during my student days.
What were the biggest surprises you encountered on your career path?
I think that when I started out on my career, as a freshly minted PhD grad, I had envisaged a pretty linear career path. What I found, however, is that one’s career can take many twists and turns along the way – it’s rarely a linear progression.
Sometimes, those are personal choices – you want to go in a different direction, you have a more appealing opportunity elsewhere – and sometimes, they’re imposed on you – companies go out of business, roles are eliminated etc.
Either way, I’ve learned that you need to keep a very flexible outlook on your career and be prepared to deal with the bumps in the road when you encounter them. When you do take a knock, you need to dust yourself down and start again.
What do you enjoy about your job?
My current role is a great mix of finding interesting solutions to challenging problems in the here and now, as well as being more forward-looking to try to understand the art of the possible, what the future might look like and how can we shape that future.
As a technologist at heart, having the opportunity to work with the latest and greatest technologies in the AI space – such as H2O.ai, TensorFlow etc – keeps the job interesting and engaging.
The other fantastic aspect of my current job is the great team that I’m part of. Mastercard Labs is a global organisation, with a major R&D presence in five different countries, including Ireland. Having the opportunity to work with an international group of super-smart colleagues, equally passionate about technology and problem-solving, keeps the working environment constantly engaging.
How did Mastercard support you on your career path?
One of the benefits that I’ve seen in working for Mastercard has been that it takes career and indeed personal development very seriously for its employees. This is both through internal professional development, coaching on how to improve in the soft skills of the job, access to technical training resources, ability to travel to relevant conferences, and so forth.
The other thing that I’ve seen with Mastercard is that it encourages employees at all levels to lay out their career aspirations, and to figure out how to help them get there.
In my own case in particular, my first role within Mastercard was with our start-up engagement team. After a number of fantastic years with that team, I was very anxious to take my career back into a more technically focused role, and to get closer to my original area of expertise AI. Working with the relevant managers, I was able to do just that over time and make the lateral career move that I was looking for.
Ultimately, it is down to each of us to manage our own careers, but Mastercard is certainly a very supportive employer in giving you every opportunity to achieve those career goals.
What advice would you give to those considering a career in this area?
Artificial intelligence, machine learning and more recently deep learning are absolutely fascinating areas, in my opinion. The original question that Alan Turing posed in his famous 1950 paper – whether machines can think – is still as relevant today, almost 70 years later, as it was then.
Despite being a relatively old space in computer science terms, the space is constantly evolving, with new approaches and techniques being developed all the time.
The application areas for AI are growing all the time, and there is a wide range of different branches of AI – such as natural language processing, computer vision, deep learning, game-playing, constraint-based reasoning and many more – that are fields in their own right. If this sounds appealing to someone at the start of their career, I would say go for it!
In terms of preparing for such a career, I would definitely advise anyone looking at the space to give themselves a great grounding in the fundamentals, such as linear algebra, calculus, probability theory and statistics. With a solid knowledge of these topics, you can start to develop expertise in software development and, from there, specific expertise in AI and machine learning.
The mathematical topics I mentioned are all subjects that we study in school or college but students sometimes struggle to see the relevance of them in the ‘real world’. Working in this area, you can see how such subjects translate directly into being able to build complex systems that can solve real-world problems that are not amenable to any other solution.
Want to work at Mastercard? Check out the Mastercard careers page for current vacancies.
