I am a Data Scientist and AI Engineer working in FinCrime ML at Rabobank, with previous experience as a Data & AI Consultant at Microsoft and as a Data Scientist at Philips. My work sits at the intersection of applied machine learning, cloud data platforms, strategic analytics, Responsible AI, and business-facing delivery.
Across financial services, enterprise consulting, healthcare, CRM analytics, and research, I have worked on turning ambiguous business problems into data and AI solutions that can be scoped, designed, implemented, explained, and used in practice.
My recent focus includes financial crime machine learning, SQL-driven behavioral analytics, generative AI, large language models, NLP, Azure-based data and analytics platforms, MLOps, and model governance. I enjoy working close to both technical teams and stakeholders: translating needs into architecture, building reliable analytical workflows, and keeping ethical, explainable AI practices part of the delivery process.
I am especially interested in AI systems that are useful beyond a prototype: solutions that improve decisions, reduce operational friction, and create measurable value while remaining understandable and responsible.
How I work
Analytical, delivery-minded, and stakeholder-aware.
I like working where technical ambiguity meets business urgency: shaping the right question, building the analytical path, and communicating the result clearly enough for teams to act on it.
FinCrime ML
Machine learning for financial crime detection, risk-focused analytics, and regulated decision-support environments.
Strategic Analytics
SQL-driven analysis, KPI design, dashboarding, and scenario modelling that translate complex data into decisions.
Responsible AI Delivery
Explainability, governance, MLOps, and Azure-based AI solutions designed for enterprise-scale delivery.