Alejandro Fontal, PhD
Data Science · Machine Learning · Health & Life Sciences
Data scientist with a PhD and 8+ years of industry and research experience in health and life sciences, currently leading the data science strategy in a MedTech R&D team. I approach data as a scientist: starting from a clear question, designing the study and validation before trusting a result, and keeping every analysis reproducible. My work spans statistical modelling, machine learning, epidemiology, and bioinformatics, applied to clinical, registry, surveillance, omics, and biomedical signal data.
Experience
Senior Machine Learning Engineer NIMBLE Diagnostics Lead the data science strategy in a MedTech R&D team developing diagnostic methods from biomedical signals collected in experimental studies and clinical trials.
- Develop statistical and machine learning methods to extract diagnostic information from complex biomedical signals in experimental and clinical-trial data.
- Define validation and performance-assessment strategies anchored to clinically meaningful endpoints.
- Work with clinical, signal-processing, and engineering teams on how biomedical measurements are processed, represented, and modelled, and on how model outputs translate into clinical results.
Postdoctoral Researcher · Data and Software Lead Climate & Health Program @ ISGlobal Combined postdoctoral research with responsibility for software, data workflows, and reproducibility across climate-health and aerobiome projects.
- Led software and data engineering for the group, building internal packages, web tools, and GitHub-based workflows for reproducible research.
- Built reproducible pipelines for long-read aerobiome metagenomics and linked results to health outcomes.
- Developed machine learning models for bacterial discrimination from laser-induced fluorescence and light-scattering measurements, enabling near-real-time identification.
PhD Fellow · Marie Skłodowska-Curie Early-Stage Researcher Climate & Health Program @ ISGlobal (HELICAL ITN) Applied statistical and computational methods to large observational health datasets across epidemiology, climate-health, and disease-surveillance studies.
- Worked with population-level health data from disease registries, emergency-care datasets, biobanks, and infectious-disease surveillance systems, including Japanese Kawasaki Disease registries, Catalan acute myocardial infarction emergency data, the Japanese out-of-hospital cardiac arrest registry, and UK Biobank.
- Studied incidence, subgroup differences, temporal and geographic patterns, and associations between environmental exposures and disease onset using statistical, time-series, and spatial methods.
- Integrated heterogeneous health, demographic, environmental, and geospatial datasets, including sources with changing reporting practices, coverage, and data quality.
- Built reproducible Python and R pipelines for data processing, statistical analysis, and research outputs.
Data Scientist Protein Engineering @ DuPont Industrial Biosciences Embedded machine learning and reproducible pipelines into protein engineering workflows with wet-lab partners.
- Integrated data-driven and machine-learning solutions into protein engineering workflows.
- Built and maintained reproducible bioinformatics and data pipelines.
- Partnered with wet-lab teams to design experiments and close the model-experiment loop, accelerating workflow automation.
Data Science Intern DuPont Industrial Biosciences Internship focused on machine learning for empirical protein design and model benchmarking.
- Built, trained, and benchmarked deep learning models to predict enzyme performance from sequence and structure.
Assistant in MOOCs Development Educational Staff Development @ Wageningen University Produced assignments and technical support materials for large-scale online course delivery.
- Developed assignments and technical content for 10+ edX MOOCs.
- Provided technical support across course development workflows.
Education & Languages
Education
PhD in Biotechnology
Universitat de Barcelona
- PhD focused on environmental determinants of disease onset, spatiotemporal modeling, and long-read aerobiome metagenomics.
MSc in Bioinformatics
Wageningen University & Research
- Data Science minor with cum laude distinction.
- Thesis on interpretable deep learning for protein subcellular location prediction.
BSc in Biotechnology
Universitat de Barcelona
- Thesis at the VHIR Bioinformatics Unit on meta-analysis of transcriptomics tools.
Technical & Analytical Skills
Statistical & Health Data Analysis
Experience with clinical, registry, surveillance, biobank, and other observational health data. Statistical work includes incidence and subgroup analyses, time-series and spatiotemporal modelling, heterogeneous data integration, and data-quality assessment.
Machine Learning
Supervised and deep learning across tabular, sequential, biological, and biomedical signal data, covering model development, benchmarking, validation, and performance assessment.
Python
Primary language for data analysis, statistical modelling, machine learning, package development, APIs, and data tooling. Regularly use pandas, polars, statsmodels, scikit-learn, PyTorch, TensorFlow, plotnine, and FastAPI.
R
Proficient with R for statistical analysis and bioinformatics, particularly tidyverse and Bioconductor, with regular R/Python interoperability in analytical workflows.
Scientific Software & Reproducibility
Build and maintain packages, reproducible data pipelines, tests, code-review workflows, CI/CD, research-facing APIs, and analytical tools using GitHub, GitLab, Jupyter, and Quarto.
Linux, HPC & Infrastructure
Daily Linux user across local, remote, and HPC systems. Work includes shell scripting, SLURM and Nextflow workflows, Docker and containerized deployment, service automation, secure remote access, and lightweight server infrastructure.