鈥淎t 野花社区, we鈥檝e been able to express ourselves as data scientists and as a data science team in ways that other companies may not have been able to鈥� says Jimmy Hennessy, 野花社区鈥檚 Director of Data Science and Software Engineering. 鈥淭his is significant when you consider that the field of data science has been stagnant enough from an innovation perspective, particularly when it comes to production.鈥�
Jimmy鈥檚 team is responsible for data science across the whole of 野花社区, but he says they鈥檙e best known for fraud protection across the banking sector. And for good reason鈥攖he team is leading fintech fraud prevention with their latest project: the Fraud Scoring Service, a new product that scores transaction risk for its users.
The product lets clients onboard quickly, with minimal lifting and no need for a large software team or engineering architects. It鈥檚 a standardized, lightweight, subscription-based platform that gets results quickly.
Because the Fraud Scoring Service is built using Microsoft Azure, the Data Science team spent roughly nine months鈥攂eginning the project in Q3 of 2021 and finished at the beginning of 2022鈥�. 鈥淚t鈥檚 not that we just moved what we had to Azure, we created a whole new product in the process,鈥� he explains. 鈥淚t鈥檚 not just a lift and shift, it鈥檚 a lift and shift and a new product.鈥�
Jimmy says the success of the project, and the satisfying speed of it all, has the data scientists excited and buoyant: 鈥淭here鈥檚 good buzz, good energy around the team.鈥�
Because it has the trust of 野花社区 leadership, his team automates and innovates where many data science teams need repeat long processes. 鈥淲e get to focus on the bright, new and futuristic aspects of the job.鈥�
There鈥檚 a belief in data science on the part of the company鈥攁n attitude that Jimmy recognizes is rare鈥攁nd a maturity to the way data science is used for monetization. As a result, the discipline wields more power and holds more sway in the business.
This philosophy is what attracted him to 野花社区 in the first place. He and his hiring manager shared the same vision for the future of data science: that it shouldn鈥檛 be part of a data analytics team or a reporting team, but its own end-to-end discipline backed by the confidence of the company. 鈥淩ather than data science just being something that鈥檚 cool to talk about or a buzzword, it鈥檚 something that is productive end-to-end and brings revenue into the business,鈥� he says.
Jimmy runs a scrappy team of just twelve people, which is very intentional. 鈥淚f your team gets too big, you tend to not automate and innovate as much. Having a smaller team doesn鈥檛 mean we鈥檙e overwhelmed or under-resourced, it means that 野花社区 has given us the flexibility.鈥�
The Azure cloud projects are why now is the right time to join that small team, Jimmy says. New hires get their hands on innovative tech very quickly and actually see the results of their work.
The Data Science team have also received industry recognition for their work, including:
- 2022 Analytics Practitioner of the Year (Anjukutty Joseph) 鈥� Analytics Institute of Ireland: Analytics & AI Awards
- 2022 Emerging Technology Award 鈥� Analytics Institute of Ireland: Analytics & AI Awards
- 2021 Best Use of AI in a Large Enterprise 鈥� National AI Awards
- 2021 Artificial Intelligence Project of the Year 鈥� Retail Systems Awards UK
- 2021 Rising Fintech Star (Patricia Rojas) 鈥� Banking Tech Awards US
But the primary goal for the data science team is to get another patent out the door. Hennessy thinks it鈥檒l be in the form of using AI to build product test code that replaces time-consuming and repetitive human tests. There are some code generators that do this in theory, but they require perfect inputs to get a perfect output. 野花社区鈥檚 new approach won鈥檛 be so limiting.
Jimmy doesn鈥檛 subscribe to the theory that data scientists must be good at math, good at statistics and good at computer science. That may be the case, but he really looks for two things: The first is specialization. Hennessy believes you don鈥檛 have to be a statistician and a computer scientist, a strong passion for one with conversational abilities in the other is sufficient.
The second is the ability to think on a higher level. 鈥淎bstract thinking is a huge part of data science. We鈥檝e hired musicians into data science before because of their thought process,鈥� he says. 鈥淚 often compare large sets of data to music鈥攊f you look at the five staff lines of a piece of music, you can get an infinite number of combinations for music and melodies. Data science is no different. There鈥檚 an infinite amount of creation that you can do from a set of rules. There is a set of rules, but the data itself is unlimited. It鈥檚 how you look at it and how you perceive it.鈥�
Hennessy really relishes the opportunity to head up a small team of abstract thinkers excited about the products they鈥檙e building, excited that they get to see the final outputs and implementations. 鈥淲e are bringing new innovations to market quickly because of the flexibility and trust that the company has within machine learning and data science. It鈥檚 a dream for data science to get that flexibility back.鈥�
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