Machine learning architect focused on data science productivity, reliability, performance, and cost.
Working on designing and implementing large scale AI products through data collection, analysis, and warehousing.
Passionate about building scalable machine learning pipeline architectures with high business impact.
Over the years, he led the architectural design, implementation, and maintenance of multiple Data Science Platforms used by hundreds of data scientists, business analysts, and product owners.
He led the design and implementation of a large-scale web advertisement click prediction system, that handles 100s of billions of daily impressions with millions of dollars of daily spend.
He also guided the design of a set of classical and deep machine learning systems, related to business-critical ad-tech products such as: ad-creative image classification, website content categorization, and embeddings-based audience recommender systems.
He was involved in the design, architecture, and technical implementation of hundreds of data reporting pipelines and webapps/APIs, to deliver critical financial and product metrics through dashboards for hundreds of stakeholders.
He enjoys teaching and he has mentored, trained and supported 100+ data scientists and business analysts on software engineering best practices, based on the Clean Code design methodology.
He holds a PhD in Computer and Information Science from Temple University, USA. He graduated with a Master in Information Technology from Lappeenranta University of Technology, Finland, with a major in communication engineering and a minor in marketing and information processing.