Berlin, Germany

BenjaminMoreno Torres

Data Science & AI Lead

About

Professional portrait of Benjamin Moreno Torres

Data and AI leader with 10+ years applying machine learning to R&D problems in materials science, structural monitoring, and market intelligence. Trained as a mathematician and an architect, I specialise in sequential learning for materials discovery, ontology engineering, and NLP, backed by hands-on Python and MLOps practice. At TU Berlin I led the building-demonstrations work package of a Horizon Europe project across six partners in four countries, alongside the project's AI and data research. I am looking for a Data Science or AI Lead role in an organization with strong R&D.

Experience

April 2023 - May 2026

Research Associate, AI & Data · Work Package Leader

Technische Universität Berlin · Horizon Europe project REINCARNATE (circular construction materials) · Berlin, Germany

Led the Demonstrations in Buildings work package, coordinating 6 partner organizations across 4 countries and owning deliverables, timelines, and integration of results.
Co-authored SLAMD (Sequential Learning App for Materials Discovery), an open-source active-learning tool for sustainable materials design (NanoWorld Journal, 2023), and coordinated its industrial application in the project.
Designed an ontology-based data architecture (OWL/RDF/SPARQL) for FAIR interoperability across heterogeneous research datasets.
Developed probabilistic life-cycle models for construction products; first author of project deliverable D1.3 on probabilistic methods for life-cycle assessment.
Supervised 4 graduate researchers in AI and digital construction, guiding projects from scoping to publication-ready outputs.
Managed stakeholder communication across academia, industry, and EU project officers.

Horizon EuropeOntology EngineeringPythonFAIR DataSequential Learning

2020 - January 2023

Machine Learning Researcher

BAM - Federal Institute for Materials Research · Berlin, Germany

Built predictive ML models (clustering, regression, ensembles) for material durability and circularity assessment.
Structured and analyzed databases on Alkali-Activated Concretes from heterogeneous experimental sources.
Developed data pipelines for EU digitalization projects, improving cross-institutional data quality.
Collaborated with chemists, physicists, and engineers to translate domain expertise into ML feature engineering.

Machine LearningMaterials SciencePythonData Pipelines

2016 - 2020

Data Scientist & ML Engineer

3S'TECH S.L. · Girona, Spain

Developed predictive algorithms for real-time structural health monitoring using IoT sensor networks.
Designed end-to-end data pipelines for sensor data ingestion, cleaning, transformation, and anomaly detection.
Implemented ML models for engineering databases and concrete testing.

IoTStructural Health MonitoringML EngineeringPython

Aug 2012 – May 2016

Data Scientist - NLP & Analytics

Symanto GmbH · Nürnberg, Germany

Built Python and C# tools for social media sentiment analysis and market research.
Contributed to NLP pipelines (text classification, entity extraction) and graph-based influencer analytics.
Delivered data insights for digital marketing campaigns.

NLPPythonC#Graph Analytics

2005 - 2011

Structural Designer & Architectural Engineer

Various Firms · Spain

Structural design, planning, and valuation across residential, heritage, and public projects including the Disseny Hub Barcelona.
Developed systems-thinking and spatial reasoning skills that now underpin a distinctive approach to data architecture and ML system design.

ArchitectureStructural EngineeringSystems Thinking

Featured Project

SLAMD - Sequential Learning App for Materials Discovery

Open-source AI tool for inverse design of sustainable cementitious materials

SLAMD applies sequential (active) learning to accelerate the discovery of sustainable cement and concrete formulations. By iteratively selecting the most informative experiments, it reduces the number of lab tests needed to find optimal material compositions - making R&D faster and more sustainable.

Role: Co-developer, tester, and co-author. Contributed to design, validation, and the NanoWorld Journal publication (2023).

View on GitHub ->Live Demo ->Read Publication ->

Sequential LearningActive LearningMaterials SciencePythonOpen Source

Skills

ML / AI

Sequential / Active Learning
NLP
Graph Analytics
Clustering
Regression
Ensemble Methods
LSTMs
Probabilistic Models (Markov Chains, Bayesian Networks)

MLOps & Production

Docker · Kubernetes
MLflow · DVC · Airflow
FastAPI · BentoML (model serving)
Prometheus · Grafana (monitoring)
CI/CD pipelines · Git/GitHub
LangChain · LLM integration

Data & Semantic Web

Ontologies (OWL, RDF, SPARQL)
FAIR Data Principles
Semantic Web
Linked Data
Data Pipeline Design

Leadership & Project Management

Horizon Europe
Multi-stakeholder coordination
International consortium management
Graduate researcher supervision
Technology transfer

Programming

Python (primary)
R
C#
SQL

View publication ->

Academic publication

A case study of structural monitoring as a control tool in the restoration process of Heritage Structures: The strengthening of the Vistabella Church's Tower

UPCommons

View publication ->

Education

MLOps Specialty (Machine Learning Engineer)

Liora / DataScientest, certified by Université Paris La Sorbonne

Oct 2026

PhD, Semantic Web Applications for Materials Discovery

Technische Universitat Berlin

Ongoing

Master in Architecture (375 ECTS)

Universitat de Girona

2005 – 2011

Master in Mathematics for Industry (60 ECTS)

Universitat Autonoma de Barcelona

2001

Degree in Mathematics (300 ECTS)

Universitat Autonoma de Barcelona

1994-2000

Contact

Open to conversations about Data Science and AI Lead roles in Berlin and the DACH region. Hybrid or remote.

benji.moreno@gmail.com Connect on LinkedIn