Senior Applied Scientist & Software Engineer helping early-stage startups transform cutting-edge research into market-ready products. 15+ years building AI/ML systems that solve real business problems across 6 countries and multiple industries.
Specialized in: Taking startups from founders' market insights to a minimum viable product (MVP) in 3-6 months using artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and search technology.
I am a senior applied scientist and software engineer with 15+ years of experience transforming academic research into operational software. I have been in the role technical founder or founding engineer at multiple early-stage startups in Iceland, Netherlands, United Kingdom, and Spain with additional participation in the startup scene in Germany and France.
Track record: Built and deployed AI/ML systems and data pipelines that have processed millions of data points, to provide, among other things, age-appropriate search results for children, market insights for real estate investors, and impact information for urban planners.
Perfect for startups that need: A technical leader who can bridge the gap between cutting-edge research and market-ready products, fast.
From idea to product in 3-6 months:
Training, fine-tuning, and evaluation of task-specific machine learning systems (ML) and large language models (LLMs).
Development and evaluation of ranking algorithms, including their deployment as search engines and retrieval-augmented generation (RAG) applications.
Data pipelines (ETL + APIs): Focused web crawls, data extraction, ingestion into knowledge bases or other data stores, and deployment of data services via APIs.
Spatial Informatics: Processing and interconnecting heterogeneous geographic datasets to provide deeper understanding of the world around us, with a special focus on environmental sustainability.
Prototypes and minimum viable products (MVPs) for extracting data-driven insights and rapid business model validation.
Web crawling and information extraction (using LLMs) to build an explorable knowledge base providing insights into the impact of urban interventions and policy decisions (Urbanixm).
Intelligent document processing to facilitate AI powered post-trade processing for financial institutions (Banqora).
Discovery of diverse data sources related to commercial real estate, ingestion into an interoperable data-format and serving via APIs (Placemake.io).
Machine learned estimation of reading level needed to understand a piece of information (WizeNoze).
Using machine learning to identify and disambiguate brand references in media articles (brandcrumb).
Interactive data-stories on a range of topics for various clients:
Analyzing the pricing of energy performance certificates in residential real estate.
Identifying opportunities for symbiotic CO2 recycling.
Exploring carbon-insetting opportunities in the fashion sector.
Generating knowledge graphs from diverse sources to enable faceted exploration of image search results [WWW 2010 | pdf] (Yahoo! Research / Yahoo! Image Search).
Facilitating exploration of Flickr photos through semantically structured tag clouds [summary | technical report] (Yahoo! Research).
Providing focused access to sub-article-level information from Wikipedia through a specialized search engine [DIR 2006 | pdf] (University of Amsterdam)
Get in touch on LinkedIn or drop me an email for more information about rates and availability.