Maria Vounou is Director of Data Science at Burberry, where she leads the company’s central, globally focused Data and Analytics function. Her career is grounded in a strong academic foundation, having studied Mathematics before completing a PhD in Statistics. Her doctoral research applied advanced statistical methods to high-dimensional brain imaging and genetic data, building deep technical expertise in analytics, modelling, and applied AI.
Maria joined Burberry over a decade ago as a junior data scientist, at a time when data science was still emerging both within the industry and the organisation. Over 13 years, she has played a pivotal role in establishing Burberry’s data capabilities from the ground up by helping to define the technical foundations, expand the data footprint, and embed analytics and AI within a creative-led, luxury retail environment. Her progression from individual contributor to senior leader mirrors the evolution of data as a strategic capability within the business.
Today, Maria leads a multidisciplinary team of data scientists, analysts, and data product managers. She is responsible for setting the enterprise data, analytics and AI agenda in partnership with the executive team, prioritising initiatives and implementing data-driven solutions that drive value, enhance decision-making and power Burberry’s strategy.
Driven by a passion for mathematics, statistics, and problem-solving, Maria is particularly focused on translating advanced analytics into tangible business impact while maintaining technical excellence. She has previously been listed in the DataIQ 100, and her team has received multiple DataIQ Awards, including Best Data and Analytics Team, Innovation Champion, Data-Enabling Solution of the Year, Transformation with Data and Most Innovative Use of AI.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
Maria anchors effective data and AI leadership in people, discipline, and partnership, with a strong emphasis on building and empowering high-performing teams. She described leadership impact as something that is “amplified through people” and argues that “data and AI outcomes are ultimately driven by them”. She focuses on recruiting and developing top talent, creating an environment of trust, and enabling individuals to perform at their best. This people-first approach has proven critical in maintaining continuity, resilience, and strong capability through periods of organisational change, allowing the team to “continue delivering even more value under pressure”.
The second trait for Maria is rigorous prioritisation. Particularly in constrained environments, she believes that “more work does not necessarily mean more value”. Effective leaders must be disciplined about saying no, protecting their teams from low-impact activity, and “maintaining focus on business value”. This clarity of focus not only improves business results, but also ensures efforts are directed where it matters most.
The third capability is strong partnership and trust across the organisation. Maria emphasised the importance of “meeting stakeholders where they are”, practising active listening, and co-creating solutions so that insights translate into action rather than remaining theoretical.
Taken together, these traits reflect a leadership style that values sustainability over heroics by investing in people, concentrating effort on the highest-value opportunities, and building the relationships needed to turn strong analytical capability into lasting business impact.
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
Maria’s advice centres on a subtle but powerful shift in mindset: prioritise being trusted over being right. She candidly stated that, while technical mastery can feel like the hardest part of the job, “it’s actually not what drives impact on its own”. The greater challenge sits in influence, adoption, and the ability to bring others along.
Data leaders are often deep in the detail, confident in the robustness of their approaches, and rightly proud of their technical work. That can make it tempting to focus on proving a solution is right and expecting adoption to follow. Her experience has taught her that correctness alone does not equal impact.
Reflecting on earlier examples, Maria noted that even the most technically sound work will stall if trust is missing. The question, therefore, becomes less “how do I convince people I’m right?” and more “how do I build trust and influence to make change happen?” For Maria, this means investing in relationships and framing work in simple terms that resonate with business priorities. Communicating value clearly, making work visible and relevant, showing how benefits will materialise, and securing small incremental wins are what ultimately drive trust and adoption.
Maria believes teams should still recognise and celebrate technical achievements, but for leaders aspiring to the C-suite, she argued, success is defined by whether that work is used and creates change. In practice, that requires a conscious shift away from defending methodology and towards building partnerships, telling a compelling value story, and earning the trust that allows strong ideas to land.