Background
At The Tech Collective - part of Implement Consulting Group's Digital Transformation practice - I combine hands-on engineering with AI advisory. I build end-to-end AI solutions across the full stack, from data pipelines and model training to application layers, while advising clients on AI strategy: where it fits, what to prioritise, and how to move from idea to production.
Promoted to Senior Data Scientist at Stibo Systems, where I led AI projects and helped shape the company's ML direction. I architected end-to-end solutions running in production for Fortune 500 customers, set the technical bar for the data science team, and mentored junior data scientists. Alongside delivery, I drove the modernisation of the underlying ML platform - feature management, model registry, CI/CD, and infrastructure-as-code - to shorten the path from prototype to production.
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Elected as an alternate member of the Board of Representatives at IDA - Denmark's largest engineering association.
Part of the team organising the Driving AI and Driving IT conferences within IDA IT since January 2025. I joined the Steering Committee of IDA AI in December 2025 and have since taken over as chair of its board.
Volunteered at ReDI School of Digital Integration, teaching the Data Analytics with Python track and contributing to the curriculum and learning materials. Separately, I mentored a student one-on-one on career strategy in the tech industry.
Joined Stibo Systems to deliver AI-driven solutions for enterprise clients. I worked directly with stakeholders to frame business problems and prototype high-impact use cases, then took the most promising ones into production. I led the rollout of a centralised Feature Store and standardised ML pipelines on MLflow and GitLab CI/CD, improving reproducibility across teams. Alongside delivery, I mentored junior data scientists and collaborated with universities through the Stibo Systems Accelerator.
Click to view detailsMSc at the Technical University of Denmark (DTU), specialising in machine learning for computer vision and latent variable models. As an honorary student under Ole Winther, I took three research-level courses (30 ECTS), earning top grades (12/A) in all. I worked closely with Giorgio Giannone throughout, gaining hands-on experience in advanced probabilistic modelling and deep generative models. My master's thesis developed optimisation techniques to accelerate inference in denoising diffusion probabilistic models for image generation.
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I started my career in Terma's AI team, working on projects that blended data engineering, visualisation, and machine learning. I built ETL pipelines for anomaly detection in maritime surveillance systems, designed GIS-integrated dashboards to visualise live ship positions, and deployed computer vision models on embedded edge devices for real-time detection under tight resource constraints. It was where I first learned what it takes to move an AI idea from concept to something that actually runs in the field.
Exchange semester at the University of Maryland, College Park, where I took my first machine learning course - the one that pulled me toward AI and data science. I finished the semester with academic honors.
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BSc in Electrical Engineering from the Technical University of Denmark (DTU). During my studies, I developed a strong foundation in physics and engineering principles and was introduced to programming, a field that quickly captured my interest. I served as a teaching assistant in engineering mathematics for one year, an experience that enriched my understanding and ability to communicate complex technical concepts. My bachelor's thesis focused on the design and implementation of Chora, a web-based instruction set simulator for patmos.
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