Jose Antonio Jimenez

▪ Defined the AI methodology for AI - Team Data Science Process (TDPS), aligning TDSP lifecycle phases with internal standards

for CI/CD, storage layers (Bronze/Silver/Gold), and S3 structuring.

▫ Created and maintained multiple data reports and architecture documentation, including configuration files and dataset

contracts under TDSP standards.

▫ Supported synchronization of tasks among teams, and formalization of development methodology for onboarding and

scalability.

▪ Developed reusable Kubeflow pipelines and explored MLflow deployment alternatives, integrating local MinIO simulation and

Docker Compose setups.

▪ Defined the official AI training platform integrating emitter disentanglement models, waveform generation, model training, and

inference pipelines over OpenShift AI.

▪ Led the review and QA of code repositories and pipelines, identifying maintainability risks and proposing modular orchestration

and API-based architecture.

▪ Provided continuous technical support and coordination for SuperPOD GPUs infrastructure, including resource availability,

execution tracking and SLA definition.

▪ Coordinated integration with Waveblue/Umbrella platforms, ensuring compatibility, secure data flow, and interoperability.

▪ Participated in sensitivity analysis of data flows and setup of secure development environments.

▪ Contributed to dataset standardization and mapping, helping unify the generation and transformation workflows for data.

▪ Collaborating with Algorithmic Transparency Unit at the Joint Research Centre (JRC) as AI/ML/NLP Specialist, leading technical contributions to two data science work strands requiring large-scale data analysis had a significant impact on the project's success.

▪ Single developer of an end-to-end, automated data infrastructure designed to disaggregate, classify, and analyse customs records to identify secondary material flows critical to the EU’s circular economy, with a focus on titanium and photovoltaic (PV) panels. Pipeline. Advanced Natural Language Processing (NLP) techniques, transformer-based topic modeling using BERTopic, and fuzzy logic.

▪ Development of machine learning/deep learning/GenAI algorithms for economically complex research challenges at the Joint Research Center (JRC), the science and innovation service of the European Commission.

▫ Economic analysis of investment in R&D in the European territory (Patent Matching)

▫ Development of simulation and prediction models for complex energy scenarios (SIACES).

▫ MLOps in MS Azure Machine Learning with TDSP.

▫ Collaboration with the Pacific Northwest National Laboratory (PNNL) and Tufts University to model prediction in 1.5 °C global warming climate change scenarios.

▫ Exploratory Models of the Impact and Innovation ecosystem to identify new technologies and/or fields of activity (industrial sector or services)

▫ Extraction of information related to news about Food Fraud (web scraping and ETL).

▫ NLP/LLM classification modeling of extracted data on Food Fraud using AI (unsupervised classification). Publication of results through dashboard in PowerBI.

▫ Development of anomaly detection and alert generation systems using generative AI and machine learning algorithms such as Isolation Forest and Z-Score.

▫ Integrated NVIDIA CUDA to accelerate computational tasks, optimize performance, and enhance the efficiency of machine learning models and data processing pipelines.

▪ Definition and execution of a data science strategy for pharmaceutical manufacturing, combining advanced machine learning methodologies with industry expertise to drive innovation and ensure regulatory compliance.

▪ Design and implementation of predictive models for Lot End Prediction, focused on forecasting batch completion inpharmaceutical production environments. These models enhanced operational planning, increased resource efficiency, and reduced production waste.

▪ Development of machine learning solutions to optimize manufacturing processes, improve yield, and prevent failures, aligned with regulatory frameworks (FDA, EMA, GxP).

▪ Execution of data engineering and modeling workflows using Python, and scalable cloud platforms (AWS, Azure), integrating data from laboratory systems, production lines, and quality control processes.

▪ Collaboration with cross-functional teams—including manufacturing, quality, compliance, and IT—to translate domain requirements into robust data-driven solutions.