Alejandro Fontal, PhD
Data Science · Machine Learning · Health & Life Sciences
Computational scientist and Senior Machine Learning Engineer working across health and life sciences, with experience in statistical modelling, machine learning, bioinformatics, epidemiology, biomedical signals, and scientific software.
Development Skills
Python
Expert (9+ years); primary language for package development, testing, and end-to-end analytical and ML work. Regularly use pandas, polars, statsmodels, scikit-learn, TensorFlow, PyTorch, plotnine, and FastAPI.
R
Proficient with tidyverse and Bioconductor, with strong R/Python interoperability in statistical and bioinformatics workflows.
Linux + HPC
Daily Linux user with scripting, remote systems, and HPC workflows using SLURM, Nextflow, and reproducible command-line pipelines.
Git + CI/CD
Collaborative development, code review, and automated testing and deployment with GitHub Actions and GitLab CI/CD.
Web + APIs
Development of lightweight web tools and REST services for research and data applications using HTML/CSS, JavaScript, and FastAPI.
Systems
Linux-based deployment for data and research applications, including containerized services, automation, secure remote access, and lightweight infrastructure.
Experience
Senior Machine Learning Engineer NIMBLE Diagnostics Leading the machine learning strategy for a MedTech R&D program based on complex biomedical signals. Jul 2026 - Present Barcelona, ES
- Develop statistical and machine learning approaches to extract clinically relevant information from experimental and clinical biomedical measurements.
- Define the modelling workflow from understanding the measurements and developing useful representations through model development, validation, and performance assessment.
- Work with signal-processing, clinical, and R&D teams to connect measurement behaviour and model outputs with clinically meaningful endpoints.
- Build reproducible Python workflows for exploratory analysis, model development, and validation.
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. Oct 2024 - Jul 2026 Barcelona, ES
- 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 · Early-Stage Researcher Climate & Health Program @ ISGlobal (HELICAL ITN) Developed computational methods and field workflows to connect atmospheric processes with infectious disease dynamics. Oct 2019 - Sep 2024 Barcelona, ES
- Developed time-series methods to quantify environmental signals in epidemics, including transient and lagged associations, multi-scale correlations, and phase-shift analyses.
- Integrated nationwide case data with climate reanalysis, remote sensing, GIS layers, and air-mass trajectories to study Kawasaki Disease, COVID-19, and influenza.
- Coordinated field sampling campaigns in Japan to characterize the biological and chemical composition of air masses.
Data Scientist Protein Engineering @ DuPont Industrial Biosciences Embedded machine learning and reproducible pipelines into protein engineering workflows with wet-lab partners. Oct 2018 - Sep 2019 Leiden, NL
- 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. Mar 2018 - Sep 2018 Leiden, NL
- 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. Sep 2016 - Dec 2017 Wageningen, NL
- 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.