Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Bruno:
I am a London-based Senior Data Scientist and technical lead with a PhD in Electrical Engineering and a career shaped by product impact, technical depth, and curiosity across multiple industries. My work spans rail technology, mobile gaming, and e-commerce, with experience at Trainline, King, and Magazine Luiza, where I have applied data science to large-scale product, commercial, and customer experience problems. I specialise in product-focused data science, particularly experimentation, recommendation and personalisation systems, search and conversion optimisation, customer journey measurement, and scalable analytics infrastructure. Across these environments, I have helped teams move from raw behavioural data to better product decisions, stronger experimentation practices, and more robust measurement systems.
Before moving fully into industry, I built my foundations in academia as a researcher, lecturer, and mentor, and that experience still shapes how I work today. I enjoy guiding others, translating complex ideas into something people can act on, and designing solutions that are not only technically sound, but understandable, maintainable, and scalable. I bring a strong problem-solving mindset and a systems-design view to data science, combining statistical and machine learning methods with practical engineering judgment.
Outside work, I have always been drawn to videogames, not only as a player but also as someone interested in the product decisions, creative systems, economies, and communities behind them. I also spend a lot of time around specialty coffee: brewing it, learning about beans and methods, and looking for good independent cafés whenever I travel or explore a new city. These are personal interests, but they also say something about how I tend to approach things more broadly: with curiosity, patience, and attention to craft.
How did you start out in your tech career?
I started programming in Python and studying Deep Learning when I was in the final stage of my PhD. That ignited a long-lasting attraction towards the data science industry. Between my PhD and getting my first job in the corporate data industry, I continued acting for 2 more years as a lecturer professor, and my extra personal time was filled with several personal projects, bootcamps and online courses, sharing outcomes on LinkedIn, mentoring young students to get started with some hard-skills and statistics. The move was also motivated for personal reasons, as I took a chance in switching from a stable position in academia, in a countryside university, into a new career as a JR Data Scientist in a big retailer company from São Paulo.
My first job was really important and foundational, I had two alternatives and I opted for a lower salary in a more consolidated company – it was the right choice at the time, as I learned from very skilled data professionals within a major tech department from my home country. This accelerated my transition and I am very grateful for the patience and collaboration of the colleagues who embraced me in my journey.
What are the signs of success in your field?
To me, a Data Scientist is a problem-solver whose expertise lies in learning domain knowledge through the use of data, while framing the right problems and understanding their impact along the way.
If you’re in a company that produces large and complex datasets, you have the privilege of accessing information that can help you understand how the business actually works. Through hands-on experience across the data flow, from raw data ingestion to refined financial reporting, from marketing acquisition to personalised recommendations powered by machine learning models, you start seeing the connections between systems, users, products, and commercial outcomes.
The role models I’ve met in my professional career, and who still define excellence for me, were those who showed a thorough understanding of how data is generated inside the company, how the data landscape is structured, and how all of that connects to the challenges and strategies that business leaders are discussing and executing.
What is the best and worst thing about your job role?
When I first looked at Data Science as a career, I was fascinated by its cross-disciplinary nature: the way you can translate tools and knowledge from one industry to another, recognise similar data problems in different contexts, and navigate different sectors, departments, and problem areas.
However, this breadth can also become a curse and a distraction. There are too many tools and approaches you could use, too many problems you could choose from, and a lack of specialisation can sometimes lead to shallow professional growth if you don’t focus long enough.
You can see this tension even in the day-to-day work. If you’re embedded with product and commercial teams, you may be overwhelmed with requests and demands that distract you from your main role, which is usually to use data to influence the company towards a better strategy or solution.
So it’s tricky to find the right balance between generalising, learning, and scaling your impact, versus specialising and focusing on more complex, long-term problems.
What can you advise someone just starting out to be successful?
A key thought that I’ve been holding onto recently is being kind to yourself on your journey. We do our best to prepare for recruiting processes, learn the skills and the craft, but ultimately there are several other factors that are out of our control. Although negative outcomes affect our mood, our professional qualities are cumulative and will carry on to the next challenge.
Job-market trends, recruiting processes, and hiring platforms all have their flaws, and sometimes we can internalise those biases too. For me, it means sometimes forgetting that before I was a data scientist, I had a seven-year career in academia as a lecturer, researcher, advisor, and project manager, and I also worked for two years as a project and people manager in an NGO before I got my first job in engineering. But if you read my CV and LinkedIn profile, it might be hard to see those experiences behind the “AI, Python, Machine Learning” keyword soup we have to push forward to fit the funnel.
So TL;DR: trust in the uniqueness of your journey and the strengths you’ve been building along the way. When you do get the chance to influence the outcome, you will need those strengths to thrive beyond the surface of what everyone else is selling as table stakes.
How do you switch off?
Because data science often means long hours at a desk, deep in technical problems and screen-based work, I usually try to find the negative space of that: movement, fresh air, and time away from the computer. A gym session or a long walk in the park is often the right call to wind down. Brewing coffee at home on a slow morning, or going to one of my favourite coffee shops, also works well as a less physically demanding alternative. That said, a great game launch can also keep me entertained for many hours and become an escape from routine for a few weeks.
What advice would you give your younger self?
I would probably tell my younger self to spend less time looking for validation from external signals and other people, whether professors, managers, or mentors, and more time experimenting freely with different skills, crafts, and ways of learning. Good advice is valuable, but it should not replace self-discovery. I would encourage myself to pay closer attention to what genuinely motivates me, what kinds of problems I naturally enjoy returning to, and which activities I could see myself building around in the long term as a professional.
What is next for you?
I am reaching a crossroads where my career in data is becoming almost as long as my previous career in academia. That has prompted me to think a bit more intentionally about the next few years: which areas of expertise I want to pursue, deepen, or learn from scratch; whether there is a company or industry I want to experience and should start preparing for; and whether I want to consider managing a team and scaling my impact through others. I don’t have clear answers to all of these questions yet, but I am letting the possibilities simmer and slowly drawing the boundaries in my head as we speak. In other words, I am trying to stay prepared for when the right idea takes shape, or when the right opportunity comes across my path.
If you could do anything now, career-wise or personally what would you do? Why?
Like many people who have started reflecting on their profession since natural-language AI agents took over the conversation, I have been wondering what I would do if I ever had to pivot into a completely different space. I recently watched a YouTube video about someone in the Norwegian countryside who started roasting coffee from home and eventually left his 9-to-5 corporate job. Of course, starting a business is risky, but that story stayed with me. It made me think that, over the next decade, I would like to explore something around coffee, craft, and hospitality. Maybe it will not be roasting beans in a small flat in London, but it might not be too far from that spirit.
What are your top 5 predictions in tech for the next 5 years?
- AI-assisted work is here to stay, but it has not converged yet.
Some people are dramatically predicting that software engineering, data, and adjacent technical roles could become obsolete within a year. In practice, we are already seeing that the costs, limitations, governance issues, and integration challenges of these systems are far more complex than any clickbait LinkedIn post can capture. AI will keep changing how we work, but the dust has not settled. So buckle up: we are probably in for a few more years of AI keyword rollercoasters before stronger patterns emerge. - The tech job market will overcorrect before it stabilises.
Still on AI, but from the people and economy side, I expect the job market to remain unstable while new ways of working are still being defined. Investors and leaders are still learning where AI creates real value, where it only creates theatre, and where it introduces new costs or risks. That will probably lead to strategic mistakes in layoffs, hiring, tooling, and platform investment. In response, I expect to see more independent consultants, more one-person or small-team startups trying to surf these opportunities, and some painful reversals when companies realise they cut too deeply or invested in the wrong places. It will be challenging, but I am optimistic that the market will eventually produce organic responses to these changes. - Attention, judgement, and noise-filtering will become essential skills.
We already lived through the era of generating more data than we could reasonably process, and had to learn how to prioritise, govern, and qualify its value. I think we are about to see a similar pattern with internal corporate content. We are automating the production of reports, guides, documentation, recipes, code, and analysis, but we are not always taking the time to read it critically, refine it, and turn it into something genuinely useful. Professionals entering IC and leadership roles will have to work through this tension, especially when AI-generated content clashes with older patterns of learning, apprenticeship, and expertise-building. Knowing what to ignore may become as important as knowing what to produce. - AI-native work may finally push companies towards better architecture.
Not all of this is pessimistic. Companies may use this moment to dig themselves out of holes they previously lacked the time, motivation, or investment to address. Ideas like AI-native development and AI-assisted problem solving tend to reward a more systematic approach, where planning, architecture, documentation, and clear problem framing matter more than simply shipping as fast as possible. The difference is that implementation is becoming cheaper and faster, so the building step is shrinking. That means it will pay off more to build the right thing, rather than simply building something. - The HIPPOs are back, and data discipline matters more than ever.
More than ever, companies will need strong data-driven, experimentation, and causal-analysis cultures to avoid expensive mistakes. Need to quantify token usage across employees, agents, and services? Need to understand which agent platform performs best in time-to-ship and ROI? Want to know the actual impact and user perception of the AI feature you just launched? Data agents will help us get closer to the answers, but the specialty is still required. In the same way that data scientists will not automatically become great book writers because they have AI tools, other crafts will not become data specialists overnight just because they now have more powerful calculators.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
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