I am a data scientist who discovered my research interests through an unconventional path. After eight years managing analysis for billion-dollar operations in international finance, I found my most rewarding work lay in identifying patterns and developing analytical frameworks that revealed unexpected insights.
That investigative focus led to formal study in Data Science & AI at Goldsmiths, University of London, where I graduated with Distinction and specialized in machine learning and generative models. I now seek research roles where methodological rigour addresses meaningful societal questions.
Current Research Focus
Crisis Informatics & Computer Vision
My thesis demonstrated that modern vision models significantly outperform traditional CNN approaches for disaster image classification by 25-33% on challenging categories. "This work revealed how multimodal models, such as LLMs with vision capabilities, can recognize disaster scenarios through contextual reasoning (e.g., understanding that 'flooding + residential area = evacuation need') rather than just visual pattern matching—findings with direct implications for humanitarian response systems.
Interdisciplinary Applications
My research interests span domains where computational methods address multifaceted human questions. Whether analyzing oceanographic data for maritime search operations, developing adaptive assistive technologies, or applying pattern recognition to archaeological puzzles, the core methodological challenges remain consistent: extracting meaningful signals from incomplete, noisy data where ground truth is often ambiguous.
Background
My transition from finance to data science was deliberate and permanent. I left senior positions at P&G and Coty—where I managed analysis for portfolios including Wella Professionals—after recognizing that my interests lay in methodology rather than margins.
This pivot was validated during my MSc, where I graduated with Distinction in 2025. My thesis on improving crisis informatics through modern AI approaches combined analytical thinking developed through years of international operations with rigorous technical implementation. The work confirmed what I had suspected: I thrive when applying computational methods to questions that matter beyond quarterly earnings.
Eight years managing analysis across 40+ international markets developed capabilities directly relevant to research: pattern recognition across diverse contexts, project management under constraints, and translation of technical findings for varied audiences. These skills transfer naturally to interdisciplinary research requiring both methodological sophistication and practical feasibility assessment.
What I Bring to Research
Human Pattern Recognition
I notice when things don't fit. This has led me to uncover fraud patterns in distributor networks, identify systematic forecasting errors, and recognize that modern AI understands disaster images fundamentally differently than we assumed. My thesis emerged from observing that CNNs and language models were solving different problems when analyzing the same image.
Methodological Inquiry
Before formal training in data science, I spent years finding analytical solutions using available tools—Power BI, DAX, KNIME—to explore questions that intrigued me. While the MSc provided strong technical foundations and I genuinely enjoy implementation challenges, my particular strength lies in identifying which questions merit investigation and recognizing which analytical approaches will yield the most meaningful insights.
Practical Research Perspective
International operations taught me how analytical work translates to practice. I've seen technically sophisticated solutions fail because they misunderstood human behavior, and simple analyses prompt significant change because they addressed the right question. This helps me assess which research questions have genuine impact and which methods will withstand real-world application.
Intellectual Commitment
My progression through increasingly senior corporate roles demonstrates sustained analytical capability. I deliberately chose to transition to research—accepting junior positions to engage with more meaningful questions. Having managed billion-dollar portfolios, I now seek environments where that analytical rigor can address disaster response, accessibility technologies, or historical mysteries. The work itself, not the position, motivates my choices.
A Note on Fit
My unconventional background—senior corporate experience combined with recent technical training—positions me uniquely for research requiring both methodological rigor and practical perspective. After years optimizing profit margins, I chose to apply analytical methods to questions with societal rather than commercial value.
My research interests center on applying computational methods to human-scale problems. Years of strategic analysis in international business taught me to spot patterns and ask better questions—skills that turn out to be surprisingly domain-agnostic. The same analytical thinking that identifies supply chain bottlenecks works just as well for understanding crisis response gaps or accessibility barriers. It's this methodological flexibility I find compelling: learning to frame problems clearly and build convincing narratives from data, regardless of the specific field.
Based in Helsinki, I seek research opportunities that value methodological innovation over narrow specialization. The ideal environment would be one where diverse analytical challenges are seen as opportunities for methodological development, where questioning established approaches is encouraged, and where the goal is understanding rather than optimization.