JMAD’s CV

I am Juan Marcos Arranz Díez, a mathematician and data scientist. Here is what I have done with my life so far, at least related to work and studies:

Work Experience

February 2026 to July 2026 – Mathematical Analysis TA

• I was a Mathematical Analysis Substitue Professor at Universidad de Valladolid, taking care of half of the teaching hours for the Mathematical Analysis class for 1st year physics undergraduates. In particular, I prepared and taught exercises and wrote and graded exams. At the same time, I was a student of the Master in Mathematics, also at Universidad de Valladolid.

March 2023 to August 2025 – Data Science, ML, Python & PySpark

• I worked as a Data Scientist in the Research & Development department at Nommon, a company that mainly works with data solutions in the field of mobility and transportation.

  • I wrote several modules in Python and PySpark for a Eurostat open-source software project (GitHub repo), aiming to use cell phone big data for the production of official statistics. This involved reviewing the proposed module structure and logic, making sure it was sound, creating code requirements and tests, writing the modules and documenting them. We achieved the goal of producing a pipeline that can merge the data from different cell phone operators and keeping its execution under 24 hours.

  • I was one of Nommon’s researchers that worked on the SYNCHROMODE project. Besides being the day-to-day project coordinator from the company’s side, attending weekly meetings with the different partners and organizing workshops with stakeholders, I developed a methodology for automatic, hands-free identification of traffic states. This process used high-frequency speed data, unsupervised learning and wavelet transformation to identify changes in speed trends and estimate the traffic state at a given time. This model also was road-agnostic, allowing the usage of model trained with data from a specific set of roads to any part of the road network, irrespective of the differences between speed limits, changes in driver behavior in different areas, etc. I also gained some experience creating Docker images, as I encapsulated the whole training and prediction pipelines into one Docker image, allowing for easy deployment and usage.

  • I trained machine learning models and creating a first production version for a ML module dedicated to estimating the transport mode used by people moving in urban settings, analysing their passively generated cell phone data and performing data fusion with other sources.

Education

September 2025 to September 2026 – Master in Mathematics – Universidad de Valladolid

September 2022 to September 2023 – Master in Big Data Analytics – Universidad Carlos III de Madrid

September 2017 to July 2022 – Bachelor’s in Mathematics – Universidad de Valladolid

September 2017 to July 2022 – Bachelor’s in Physics – Universidad de Valladolid