Stirring innovation
A power blend of teamwork, data and AI (part 2)
Agile by design
Agile by design
Meet Simone, Laurent, Zhilan and Vito – a team for whom agility means rapid prototyping, continuous learning and a relentless drive to deliver new tools and services. All while staying ahead of regulatory demands and pushing the boundaries of what’s possible with data and AI.
How do they make it happen? Let's find out.
How does your team culture enable you to test, iterate and implement new concepts?
Vito: Our sandbox tech stack truly provides everything we need to prototype solutions effectively. It also makes debugging problems quick and straightforward, which is invaluable for maintaining a fast-paced and innovative workflow.
Laurent: Absolutely. The sandbox is an environment where developers, business representatives, data scientists and testers can quickly experiment with new solutions using production data. It gives us the freedom to test bold ideas without risking disruption to live systems. We can rapidly prototype, compare alternative approaches, and validate concepts before committing to full-scale implementation.
Zhilan: That’s what I also enjoy most – one of our latest initiatives is onboarding to the cloud, specifically Azure, and have access to their sandbox environment. It provides a safe space to experiment with new technologies, including AI, without impacting production systems.
We’re encouraged to cultivate an open mindset. I’m consistently given the freedom to choose the most suitable approach for delivering a project – whether it’s selecting the right data analytics tool or the underlying technology.
Simone: I’ve never been micromanaged or told how to solve a problem from the outset. This culture is something I actively promote within the Trading Analytics Team and the Data Analytics Pod.
How does strong collaboration drive innovation and delivery in your teams?
Laurent: IT engineers know the infrastructure in depth and ensure that a prototype can be materialized into a reliable, production-level product. Product managers provide the link to the business side: they understand the goals defined by stakeholders and make sure we’re always working towards the right outcomes. Data scientists are both highly technical and closely embedded in business teams, which allows them to build prototypes quickly, validate them directly with users, and explore ideas without being constrained by IT rules. This close collaboration minimizes time to market, encourages early feedback, and supports fast, iterative improvements.
In our team, collaboration across tech, product managers, and data scientists is constant and seamless, which enables us to deliver quickly and effectively. Each group brings unique strengths to the table.
Simone: My cross-functional team setup is a textbook example of short communication channels and strong teamwork . We avoid high bureaucracy and time-consuming alignments, which result in remarkable time-to-market for project deliveries.
Also, the Trading Analytics team sits directly on the trading floor, allowing continuous exchanges with stakeholders – traders, salespeople, and client advisors – on new ideas or improvements to existing tools.
Zhilan: In our team, close collaboration with product managers and data science teams is a cornerstone of how we drive innovation and deliver impactful solutions. We maintain frequent touchpoints to ensure alignment and open communication. When a topic involves all parties, we come together promptly to discuss the requirements, challenges, and potential solutions.
Vito: I agree. As part of the data science team, I help connect stakeholders with technical solutions. We begin by gathering requirements and understanding business problems, then build prototype models designed with production in mind.
Once the prototype is ready, we work closely with the TTA team to turn it into a fully production-ready solution. This smooth handoff ensures our solutions are both innovative and practical.
Having input from both business and IT sides helps me learn from diverse expertise, improving the quality of our work, and supporting my personal and professional growth.
What ensures our AI development remains sharply focused on delivering meaningful impact?
Laurent: At UBS, AI adoption is not a side project - it’s actively encouraged by management and is becoming the backbone of how we work. Multiple initiatives and working groups explore capabilities, share knowledge, and identify pragmatic use cases that create real value. We focus on applications that improve productivity and efficiency, such as AI assistants that help IT engineers write better code faster. These solutions save time, reduce manual effort, and allow people to focus on higher-value tasks.
Vito: Our structured approach ensures AI development remains focused. We start with a brainstorming phase to analyze business requirements and determine the best-fitting model. Since we primarily work with tabular and numeric data, we use quantitative machine learning models to extract insights. We rigorously test our models and prioritize interpretability to ensure transparency and trust. This approach ensures our solutions are reliable, impactful, and aligned with our business needs.
Can you share some interesting facts from your career journey?
Laurent: One of the most important decisions in my career was moving from Belgium to Switzerland to work as a consultant for Credit Suisse. Initially, I just wanted to snowboard more – but I fell in love with the country, and the move became permanent. Workwise – back then – our responsibilities were very different. We carried magnetic tapes and rotated them in data centers during night shifts. We managed reports printed on dot-matrix printers, which jammed easily and created paper chaos. From magnetic tapes to cloud and AI – the progress has been incredible.
Simone: After graduating from a Catholic girls school, I wanted to avoid anything related to math or technology. I didn’t understand their relevance to everyday life – let alone stereotypical world of banking. By coincidence (and thanks to a lost bet with a roommate), I applied for an internship in equity derivatives trading. I didn’t know what a stock or option was and treated the interview as a learning experience. To my surprise, I got the job – the hiring manager appreciated my honesty. That internship marked the start of my banking career. I loved the fast-paced environment, team spirit, and the real-world application of math and tech. When I completed my master’s in statistics in 2015, data analytics was still a niche. Most analysis was done in Excel and VBA. I never imagined how central data would be to banking and trading.
Lastly, can you share one precious career tip?
Laurent: If your career plan is just ‘move closer to the mountains,’ you actually might end up inventing new peaks in tech too! So, pack your bags (and maybe some magnetic tapes) – you never know which adventure will turn into your dream job.
Simone: Say yes to unexpected opportunities, even if you don’t know what a stock is. And be honest during your interviews: admitting you know little (or even nothing) might just get you everything.