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Lead Data Scientist | AI Engineer
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced researcher with a working background in interdisciplinary environments, both in academia and industry. Skilled in machine learning, knowledge representation and stream processing. Python and Java developer. PhD in Geoinformatics focused on semantic interoperability and event processing from University of Muenster.
University of Münster
PhD, Geoinformatics
January 1, 2009 – January 1, 2013
Universitat Jaume I
Engineer's degree, Computer Science
January 1, 2003 – January 1, 2009
IFFE Business School
Docente colaborador
October 1, 2024 – Present
Remote
Minsait
Lead Data Scientist
May 1, 2022 – Present
OPTIMITIVE Group
Senior Data Scientist
September 1, 2019 – May 1, 2022
Vitoria-Gasteiz Area, Spain
Fujitsu Laboratories of Europe
Senior Researcher at Artificial Intelligence Research division
November 1, 2015 – August 1, 2019
Greater Madrid Metropolitan Area
Ontology Engineering Group, Universidad Politécnica de Madrid
Postdoctoral Researcher
December 1, 2013 – October 1, 2015
Madrid y alrededores, España
Institut für Geoinformatik (Ifgi), University of Muenster
Research associate
April 1, 2010 – July 1, 2013
Greater Munster Area
Institut für Geoinformatik (Ifgi), University of Muenster
Research fellow
November 1, 2009 – March 1, 2010
Greater Munster Area
con terra GmbH
Developer Intern
October 1, 2008 – May 1, 2009
Greater Munster Area
ENVISION - EU FP7 project
January 1, 2010 – December 1, 2012
The ENVISION project provides an ENVIronmental Services Infrastructure with ONtologies that aims to support non ICT-skilled users in the process of semantic discovery and adaptive chaining and composition of environmental services. Innovations in ENVISION are: on-the-Web enabling and packaging of technologies for their use by non ICT-skilled users, support for migrating environmental models to be provided as models as a service (Maas), and the use of data streaming information for harvesting information for dynamic building of ontologies and adapting service execution.
SPARQLStream & Morph streams
January 1, 2010 – October 1, 2013
SPARQL queries for data streams. Complex event processing
Agentic AI
DeepLearning.AI
June 24, 2026 – Present
Microsoft Certified: Azure Data Scientist Associate
Microsoft
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Amazon Web Services Cloud Practitioner
Amazon Web Services (AWS)
June 24, 2026 – Present
IBM watsonx Essentials
IBM
June 24, 2026 – Present
Microsoft Certified: Azure Fundamentals
Microsoft
June 24, 2026 – Present
Microsoft Certified: Azure AI Fundamentals
Microsoft
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic and research background, which might lean towards a more theoretical or R&D-focused environment. While there is industry experience, the primary focus has been on Data Science and AI. The target role of 'Backend Engineer' requires a strong emphasis on robust, scalable system design and implementation, which is only partially covered by their microservices experience. The candidate's profile suggests a fit for organizations that value deep technical expertise in AI/ML and research, potentially in a backend role that heavily supports AI/ML initiatives. However, a pure backend engineering role might require more explicit experience in core backend development patterns, distributed systems, and specific programming languages/frameworks beyond what is explicitly detailed.
Soft Skills & Operational Fit
The candidate's experience as a 'Lead Data Scientist' and 'Technical leader' suggests strong leadership, project management, and team collaboration skills. Their involvement in research projects and patent grants indicates innovation and problem-solving abilities. The diverse project portfolio implies adaptability and a proactive approach to learning new domains.