Physicist with a PhD in Astrophysics and
8+ years of experience in Python-based scientific data processing
. Experienced in developing workflows for data integration, validation and quality control, complemented by training in applied machine learning and predictive modelling.
I also bring nearly two years of industry experience coordinating technical requirements with clients and software teams. I am motivated by applying scientific rigour and analytical problem-solving to projects with meaningful real-world impact.
Developed a
Python pipeline
to estimate temperature and metal content of stars from high-resolution spectra. It uses a new approach based on luminosity to reduce degeneracy between parameters in the spectral fit. The
KOBE planet-search project
uses these results to characterise the stars.
Investigated why some stellar observations lacked a reliable correction for drifts in the instrument. Recomputed missing corrections and recovered
57 usable spectra from 16 hours of telescope time
for analysis. Contributed to tests that identified night-to-night calibration differences and informed a revised calibration strategy.
Research Scientist (PhD) · Astrophysics
2018–2023
Centro de Astrobiología (CAB), Madrid · Max Planck Institute for Astronomy (MPIA), Heidelberg
Python · scikit-learn · NumPy · pandas
Turned thousands of photometric measurements into
reliable statistical relations
widely used to characterise stars in planet-search investigations.
Conducted a statistical analysis of
2,200+ stars
using the large-scale data release from ESA’s Gaia space mission, producing
public reference data
for studies of stellar companions.
Used these data products to train and compare models in a
machine-learning application
that estimates stellar type from brightness measurements.
Gathered client requirements for energy monitoring and coordinated device specifications with suppliers and Indra’s software team to support IoT data integration.
Analysed energy use and prepared client reports to guide energy-management decisions.
Demonstrated monitoring solutions to clients and supported their use after deployment, contributing from initial requirements through to day-to-day operation.
Technical skills
Programming
Python
· NumPy · Pandas · SciPy · Astropy.
Basic knowledge of Java; academic use of C/C++, MATLAB and Mathematica.
Machine learning
Scikit-learn · Supervised learning and model evaluation.
Databases
SQL / PostgreSQL · Data validation · Data integrity checks.
Version control
Git and GitHub · Collaborative development.
Systems
Linux / UNIX · Shell scripting · macOS.
Statistics
Statistical modelling · Time-series analysis · Uncertainty quantification · R.
Documentation
Scientific reporting · Markdown · LaTeX · MS Excel.
Project leadership & communication
Led
two analytical research projects
, from proposal preparation and resource planning through data acquisition, validation, analysis and delivery.
Secured
10 competitive observing programmes as Principal Investigator
, coordinating technical requirements, schedules and instrumentation resources.
Prepared technical reports and scientific documentation for multidisciplinary audiences, delivering
35+ specialist presentations
.
Contributed to
126 scientific publications
, including nine first-, second- or third-author papers; more than 2,800 citations and an h-index of 30.
IBM DevOps and Software Engineering Professional Certificate
In progress · 2026
Completed coursework in Python/Flask, Docker, Kubernetes and OpenShift.
Education
PhD in Astrophysics
2023
Universidad Complutense de Madrid · Highest Distinction
MSc in Astrophysics
2017
Universidad Complutense de Madrid
MSc in Renewable Energies
2014
Universidad Autónoma de Madrid
BSc / Licentiate in Physics
2013
Universidad de Salamanca
Teaching & additional experience
University Lecturer
2024
Designed and taught full courses in Differential Geometry at UNIE and Introduction to Astronomy at Saint Louis University, including all teaching materials and student assessments.
Research Trainee · UCM
2017
Wrote code to estimate stellar luminosities from photometry using numerical integration.
Research Trainee · CIEMAT
2014
Made coatings for solar heat collectors that absorbed up to 94% of sunlight, and tested how heating affected their performance and durability.