About me

Mathematician with a solid analytical background and a passionate interest in the applications of Mathematics and Computer Science, specialised in data analysis, machine learning, and visualisation.

I have developed my career in the marketing intelligence industry, in scientific research, and in education. I have worked for Nielsen, the leading company in media and marketing analytics in the US. My professional journey from Mathematics to applied sciences has prompted me to develop an efficient level of problem abstraction, a practical mindset, and an adaptive taste for interdisciplinarity.

I recently enhanced my training with the MSc. Geospatial Technologies, focused on Geographic Information Systems, Spatial Data Science, and Programming. In addition to my background and experience, I have acquired a deep interest in humanities and languages.

Some insights

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    Mathematics & Statistics

    I have a strong background in pure and applied Mathematics. After almost a decade of rigurous studying Mathematics, I have developed a good level of abstraction and a creative mind when dealing with any problem. I have conducted research as part of financed research programs in my home country (Mexico) and abroad (Spain). Moreover I have experience in scientific environments, as I have presented oral and written works in my research path. In my bachelor's thesis project, I studied Differential Topology and in my personal projects and work experience I mastered applied topics, including Probability and Statistics.

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    Machine Learning

    I posses a strong understanding and +3 years experience on Machine Learning theory and methods. In my work experience at Nielsen I conducted data modelling tasks using regression methods. A key ingredient implied a deep understanding of the models used and the main methods to tailor data accordingly and present insightful results. Furthermore, I formally conducted a core study of Deep Learning and other state-of-the-art algorithms as part of my postgraduate studies.

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    Programming

    In the past +5 years, I have been able to develop my programming skills in different contexts. As a student of the BSc. Physics during two years, I was first exposed to Linux environment and to data analysis and processing with Python as part of daily tasks in the lab. Lately, as part of my postgraduate studies, I have acquried a strong knowledge of ETL and modelling methods using Python, SQL, and R. Furthermore, I posses experience in Web Technologies using JavaScript, HTML, and CSS.

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    Geospatial advocate

    Since I was a teenager, I have been deeply engaged in understanding our natural and social environments. I was able to represent my country in the International Geography Olympiad twice (Cracow, Poland 2014 and Tver, Russia 2015). Recently, I developed my technical skills in Geographic Information Systems (GIS) in integration with Web Technologies and Machine Learning, as part of my postgraduate studies. I am passionate abobut technology and its power to study and solve spatial problems.

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    Languages enthusiast

    I have always loved to learn languages. My keen interest in them has opened me the opportunity to make research stays and studies in several countries (Spain, UK, Germany). I consider myself a strong defensor of linguistic and cultural diversity.

My skills

  • Data analysis
  • Machine Learning
  • Artificial intelligence
  • ETL
  • Web Development
  • Visualisations

Resume

View CV

Experience

  1. Informatics Support Technician

    Grupo Noa's (04/2025 - current)

  2. Grant holder

    Erasmus Mundus Master in Geospatial Technologies (09/2023 — 03/2025)

    > Developed Machine Learning models to forecast the consumption of electricity in the military facilities of the UN in Sudan (SQL, R, Git) and flood classification model with Deep Learning (QGIS, R, Keras, TensorFlow). > Specialisations: Data Science, Machine Learning, Statistics (Python, R), Databases (SQL), Web Programming (JavaScript, HTML, CSS), and GIS (ArcGIS, QGIS, PostGIS).

  3. Modelling Analyst

    Nielsen (10/2022 - 08/2023)

    > Implemented machine learning modelling to drive insights from media and sales time series for US and EU clients leading the CPG sector through attribution models (Unilever, Johnson&Johnson, Ferrero).

    > Translated model results to benchmark marketing KPIs presented to the client (Power Query, Excel), to measure the impact of their marketing strategies.

    > Collaborated with international teams (data processing, modelling, and customer success teams) across multiple projects.

  4. Undergraduate researcher

    Universidad de Sevilla (09/2021– 12/2021)

    > Conducted undergraduate thesis research at the Department of Geometry and Topology.

    > Presented results at a weekly seminar held at the university.

  5. Assistant professor

    Universidad Nacional Autónoma de México (08/2019– 07/2021)

    > Taught Differential & Integral Calculus I-III at the bachelor's degrees in Mathematics, Physics, and Actuarial Sciences.

    > Prepared and presented class material. Graded exams and assignments.

Education

  1. MSc. Geospatial Technologies

    Universitat Jaume I, Spain Universität Münster, Germany Universidade NOVA de Lisboa, Portugal 09/2023 - 03/2025

    > Grade: 9.22/10.00.

    > Specialisations: Data Science, Machine Learning, Geostatistics, Databases, Web Programming, and Geospatial Analysis.

  2. Visiting Student

    University of Bristol, UK 01/2020 - 07/2020

    > Grade: First.

    > Specialisations: Partial Differential Equations, Logic, Number Theory.

    > Membership of the Bristol Data Science Society.

  3. BSc. Mathematics

    Universidad Nacional Autónoma de México, Mexico 08/2016 – 10/2022

    > Grade: 10.00/10.00.

    > Specialisations: Mathematical Analysis, Probability, Statistics, Programming, Vectorial Mechanics, Partial and Ordinary Differential Equations, Topology.

    > Thesis: Morse Homology and its Applications.

Portfolio

  • IoT prediction plot

    Modelling of IoT surveyed UN camps

    Forecast and time series

    This project is the product of an academy-industry collaboration between Universitat Jaume I and the United Nations Global Service Centre. Its main purpose is to develop a forecast model of energy consumption for the blue helmet facilities in the UNISFA mission located in the Abyei region.

  • Madrid pollution map

    Madrid pollution quarter-level trend assessment in 2023

    Data Analysis and GIS

    This project aims to demonstrate some useful data processing and analysis techniques to assess trends at the quarter level of neighbourhoods of Madrid out of individual sensor data. The techniques include spatial interpolation, vectorial datacube aggregation, and trend time series analysis.

  • Inequality map

    Traffic volume classification

    Supervised learning

    Unsupervised learning project on traffic classifiacion using Random Forest, AdaBoost, XGBoost, Deep Learning, and kNN methods.

  • Flood segmentation maps

    Flood modelling with Convolutional Neural Networks

    Deep Learning

    This project showcases a complete workflow of multispectral satellite imagery collection, processing, labelling and Deep Learning modelling to detect flooded areas, in the context of the Brazilian floods that occurred in 2024. The main techniques deployed include image segmentation in QGIS, as well as the generation of weighted masks and CNN modelling in R.

  • Video game iterface image

    Bejeweled Web Videogame

    Web development

    In this project, a Bejeweled-like web videogame inspired by the Splendor board game was developed. The handler's code is written in pure JavaScript and the styling follows a "from scratch" format in CSS to showcase full understanding of these languages.

  • Air pollution image

    Castelló pollution prediction

    Machine Learning

    This projects showcases a basic processing and analysis workflow for time series in long format across different sensors using R. Particularly, a mean-value filling of missing value was performed for a single pollution sensor through time and an Long Short-Term Memory Model was run for this particular time series. The project scope is year 2023.

  • Inequality map

    Gender Data Analysis

    Data Analysis and GIS

    Study on gender financial inequality in Mexico and Brazil. We explored oficial dataset on income by gender and state in both countries. Through data transformations and visualizations, it is possible to see the spatial distribution of income by gender, the relative (women/men) income, and the participation in labor by gender.

  • App interface with list of campings

    Campings of Castelló

    Web development

    Development of a app using jQuery mobile to retrieve the location of the user and provide a list with nearby camping sites in the province of Castellon.