Tomislav Suhina

Hi, I'm Tomislav Suhina, PhD, a results oriented data professional who learned by doing everything. Over 7+ years in industry and 5 in academia, I've gone from experimenting in a startup minded analytics department to leading teams and building solutions that are still running years later. Based in Amsterdam, I bring experience from both industry and academia across finance, commerce, supply chain, IoT, and logistics.

My journey started in academic research. I earned my PhD in Physical Chemistry from the University of Amsterdam, where I worked on fundamental understanding of molecular motion under confined conditions. I designed molecules, attached them to surfaces, and observed their behavior using extensive modeling in MATLAB and a wide variety of experimental methodologies. During my doctoral studies, I published in highly cited international peer reviewed journals. That scientific rigor and modeling mindset still shapes how I approach data science problems today. My academic experience taught me to be comfortable with accepting the unknown and using scientific methodology to systematically gather the answers required, a skill that translates directly to solving complex business problems.

I'm passionate about learning and delivering real world solutions that actually work. I prioritize robust approaches over fleeting trends and believe in clear communication with stakeholders. I believe that "simple is better than complex, and complex is better than complicated".

What I'm proud of: I've built projects from scratch (doing everything from model architecture to stakeholder management), led a 15+ person team and served as technical lead on one of Heineken's most successful analytics projects with massive business impact, and been brought back as contractor to improve solutions I originally built. That full circle moment still makes me proud and humbled.

My technical expertise includes data extraction, data validation, model & data drift detection, machine learning, deep learning, LLM agents & RAG solutions, information retrieval, model tracking/monitoring/reproducibility, mapping solutions to business KPIs, A/B testing, software engineering, and the establishment of data pipelines. I have solid understanding of MLOps best practices and experience ranging from quick experiments to mission critical production systems.

I work as an independent contractor, delivering solutions for organizations like Heineken and Rabobank. Recent projects include IoT ML systems, logistics optimization, and RAG based knowledge retrieval platforms in production. Between academic research, startup environments, and leading corporate teams, I've learned what it actually takes to get analytics working in practice. I'm open to new engagements that value substance over buzzwords.